Evidence map›Paper›PMID 35609313›Full record

SynthesisJMIR mHealth and uHealth2022

Monitoring Symptoms of COVID-19: Review of Mobile Apps.

Suzanna Schmeelk, Alison Davis, Qiaozheng Li, Caroline Shippey, Michelle Utah, Annie Myers, Meghan Reading Turchioe, Ruth Masterson Creber

Abstract readSystematic Review
In one paragraph

Synthesis in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Suzanna SchmeelkWeill Cornell Medicine, New York, NY, United States.ORCID 0000-0003-1886-3798
Alison DavisWeill Cornell Medicine, New York, NY, United States.ORCID 0000-0001-7577-5845
Qiaozheng LiWeill Cornell Medicine, New York, NY, United States.ORCID 0000-0002-9265-6212
Caroline ShippeyWeill Cornell Medicine, New York, NY, United States.ORCID 0000-0003-1944-3410
Michelle UtahWeill Cornell Medicine, New York, NY, United States.ORCID 0000-0001-7722-2275
Annie MyersWeill Cornell Medicine, New York, NY, United States.ORCID 0000-0003-2309-3070
Meghan Reading TurchioeWeill Cornell Medicine, New York, NY, United States.ORCID 0000-0002-6264-6320
Ruth Masterson CreberWeill Cornell Medicine, New York, NY, United States.ORCID 0000-0002-4238-9902

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
Should we return patient reported outcomes to human subjects participants?-exploring ethical obligationsR00NR016275 · NINR · WEILL MEDICAL COLL OF CORNELL UNIV · PI MASTERSON CREBER, RUTH MARIE · 2018 to 2020
$896k
Data-driven shared decision-making to reduce symptom burden in atrial fibrillationR00NR019124 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI TURCHIOE, MEGHAN READING · 2022 to 2024
$732k
NCI NIH HHS P30 CA008748NINR NIH HHS R00 NR016275NINR NIH HHS R00 NR019124
6 · The paper itself

Abstract

backgroundMobile health (mHealth) apps have facilitated symptom monitoring of COVID-19 symptoms globally and have been used to share data with health care professionals and support disease prediction, prevention, management, diagnostics, and improvements in treatments and patient education.

objectiveThe aim of this review is to evaluate the quality and functionality of COVID-19 mHealth apps that support tracking acute and long-term symptoms of COVID-19.

methodsWe systematically reviewed commercially available mHealth apps for COVID-19 symptom monitoring by searching Google Play and Apple iTunes using search terms such as "COVID-19," "Coronavirus," and "COVID-19 and symptoms." All apps underwent three rounds of screening. The final apps were independently assessed using the Mobile Application Rating Scale (MARS), an informatics functionality scoring system, and the Center for Disease Control and World Health Organization symptom guidelines. The MARS is a 19-item standardized tool to evaluate the quality of mHealth apps on engagement, functionality, aesthetics, and information quality. Functionality was quantified across the following criteria: inform, instruct, record (collect, share, evaluate, and intervene), display, guide, remind or alert, and communicate. Interrater reliability between the reviewers was calculated.

resultsA total of 1017 mobile apps were reviewed, and 20 (2%) met the inclusion criteria. The majority of the 20 included apps (n=18, 90%) were designed to track acute COVID-19 symptoms, and only 2 (10%) addressed long-term symptoms. Overall, the apps scored high on quality, with an overall MARS rating of 3.89 out of 5, and the highest domain score for functionality (4.2). The most common functionality among all apps was the instruct function (n=19, 95%). The most common symptoms included in the apps for tracking were fever and dry cough (n=18, 90%), aches and pains (n=17, 85%), difficulty breathing (n=17, 85%), tiredness, sore throat, headache, loss of taste or smell (n=16, 80%), and diarrhea (n=15, 75%). Only 2 (10%) apps specifically tracked long-term symptoms of COVID-19. The top 4 rated apps overall were state-specific apps developed and deployed for public use.

conclusionsOverall, mHealth apps designed to monitor symptoms of COVID-19 were of high quality, but the majority of apps focused almost exclusively on acute symptoms. Future apps should also incorporate monitoring long-term symptoms of COVID-19 and evidence-based educational materials; they should also include a feature that would allow patients to communicate their symptoms to specific caregivers or their own health care team. App developers should also follow updated technical and clinical guidelines from the Center for Disease Control and the World Health Organization.

Indexed as

COVID-19Mobile ApplicationsTelemedicineHealth PersonnelHumansReproducibility of ResultsCOVID-19digital healthdigital surveillancemHealthmobile appsmobile healthmobile health applicationmonitoring systempublic healthsurveillancesymptom assessmentsymptom tracking

Identifiers

PMID35609313
PMCPMC9162134

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.