Evaluating Network Readiness for mHealth Interventions Using the Beacon Mobile Phone App: Application Development and Validation Study

Scherr, Thomas; Moore, Carson Paige; Thuma, Philip E.; Wright, David W.

doi:10.2196/18413

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…Additionally, the role of the environment and technical infrastructure in which an eHealth technology is embedded plays a role. 29 How does environmental and infrastructure maturity affect evaluation? While we consider these factors to be aspects of the technology maturity, it would be interesting to see studies that aim to distinguish among the different types of maturity.…”

Section: Discussionmentioning

confidence: 99%

Time to act mature—Gearing eHealth evaluations towards technology readiness levels

2022

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It is challenging to design a proper eHealth evaluation. In our opinion, the evaluation of eHealth should be a continuous process, wherein increasingly mature versions of the technology are put to the test. In this article, we present a model for continuous eHealth evaluation, geared towards technology maturity. Technology maturity can be determined best via Technology Readiness Levels, of which there are nine, divided into three phases: the research, development, and deployment phases. For each phase, we list and discuss applicable activities and outcomes on the end-user, clinical, and societal front. Instead of focusing on a single perspective, we recommend to blend the end-user, health and societal perspective. With this article we aim to contribute to the methodological debate on how to create the optimal eHealth evaluation design.

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Section: Discussionmentioning

confidence: 99%

Time to act mature—Gearing eHealth evaluations towards technology readiness levels

2022

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“…While our platform, and these results, are directly applicable to campus communities, they are extensible to the reopening of businesses and communities at large as well. Although more studies are necessary to understand how variations in both the district and national levels could affect uptake in disparate populations and to develop effective mobile health implementation approaches [ 48 ], digital health interventions will likely be utilized worldwide. All organizations must make decisions on how best to integrate these tools into existing pandemic response infrastructure, as well as how to address potential concerns over data ownership and stewardship, while still reaching a critical threshold of necessary users for these tools to be effective.…”

Section: Discussionmentioning

confidence: 99%

Understanding On-Campus Interactions With a Semiautomated, Barcode-Based Platform to Augment COVID-19 Contact Tracing: App Development and Usage

Scherr¹,

Hardcastle²,

Moore³

et al. 2021

JMIR Mhealth Uhealth

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Background The COVID-19 pandemic has forced drastic changes to daily life, from the implementation of stay-at-home orders to mandating facial coverings and limiting in-person gatherings. While the relaxation of these control measures has varied geographically, it is widely agreed that contact tracing efforts will play a major role in the successful reopening of businesses and schools. As the volume of positive cases has increased in the United States, it has become clear that there is room for digital health interventions to assist in contact tracing. Objective The goal of this study was to evaluate the use of a mobile-friendly app designed to supplement manual COVID-19 contact tracing efforts on a university campus. Here, we present the results of a development and validation study centered around the use of the MyCOVIDKey app on the Vanderbilt University campus during the summer of 2020. Methods We performed a 6-week pilot study in the Stevenson Center Science and Engineering Complex on Vanderbilt University’s campus in Nashville, TN. Graduate students, postdoctoral fellows, faculty, and staff >18 years who worked in Stevenson Center and had access to a mobile phone were eligible to register for a MyCOVIDKey account. All users were encouraged to complete regular self-assessments of COVID-19 risk and to key in to sites by scanning a location-specific barcode. Results Between June 17, 2020, and July 29, 2020, 45 unique participants created MyCOVIDKey accounts. These users performed 227 self-assessments and 1410 key-ins. Self-assessments were performed by 89% (n=40) of users, 71% (n=32) of users keyed in, and 48 unique locations (of 71 possible locations) were visited. Overall, 89% (202/227) of assessments were determined to be low risk (ie, asymptomatic with no known exposures), and these assessments yielded a CLEAR status. The remaining self-assessments received a status of NOT CLEAR, indicating either risk of exposure or symptoms suggestive of COVID-19 (7.5% [n=17] and 3.5% [n=8] of self-assessments indicated moderate and high risk, respectively). These 25 instances came from 8 unique users, and in 19 of these instances, the at-risk user keyed in to a location on campus. Conclusions Digital contact tracing tools may be useful in assisting organizations to identify persons at risk of COVID-19 through contact tracing, or in locating places that may need to be cleaned or disinfected after being visited by an index case. Incentives to continue the use of such tools can improve uptake, and their continued usage increases utility to both organizational and public health efforts. Parameters of digital tools, including MyCOVIDKey, should ideally be optimized to supplement existing contact tracing efforts. These tools represent a critical addition to manual contact tracing efforts during reopening and sustained regular activity.

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“…Although test line signal intensity generally tracks with parasite density, the result should be considered only a semi-quantitative analysis of the RDT itself. Despite this, the app is ideal for low-resource uses because it requires minimal training, provides results in seconds [41], and automatically collates RDT data into a single accessible database without the errors that can arise in manual data recording. While REDCap was utilized for data storage, this platform could easily be modified for compatibility with any existing record system, including DHIS2, Open-MRS or mUZIMA.…”

Section: Discussionmentioning

confidence: 99%

“…1). Data use for this app is low, and can cost below US$1 per week [41]. However, the main limitation of the current version of the mHAT software is that it does require a consistent internet connection, which may not be available in all settings where RDT analysis is performed.…”

Section: Discussionmentioning

confidence: 99%

mHAT app for automated malaria rapid test result analysis and aggregation: a pilot study

Moore

Scherr

Matoba

et al. 2021

Malar J

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Background There are a variety of approaches being used for malaria surveillance. While active and reactive case detection have been successful in localized areas of low transmission, concerns over scalability and sustainability keep the approaches from being widely accepted. Mobile health interventions are poised to address these shortcomings by automating and standardizing portions of the surveillance process. In this study, common challenges associated with current data aggregation methods have been quantified, and a web-based mobile phone application is presented to reduce the burden of reporting rapid diagnostic test (RDT) results in low-resource settings. Methods De-identified completed RDTs were collected at 14 rural health clinics as part of a malaria epidemiology study at Macha Research Trust, Macha, Zambia. Tests were imaged using the mHAT web application. Signal intensity was measured and a binary result was provided. App performance was validated by: (1) comparative limits of detection, investigated against currently used laboratory lateral flow assay readers; and, (2) receiver operating characteristic analysis comparing the application against visual inspection of RDTs by an expert. Secondary investigations included analysis of time-to-aggregation and data consistency within the existing surveillance structures established by Macha Research Trust. Results When compared to visual analysis, the mHAT app performed with 91.9% sensitivity (CI 78.7, 97.2) and specificity was 91.4% (CI 77.6, 97.0) regardless of device operating system. Additionally, an analysis of surveillance data from January 2017 through mid-February 2019 showed that while the majority of the data packets from satellite clinics contained correct data, 36% of data points required correction by verification teams. Between November 2018 and mid-February 2019, it was also found that 44.8% of data was received after the expected submission date, although most (65.1%) reports were received within 2 days. Conclusions Overall, the mHAT mobile app was observed to be sensitive and specific when compared to both currently available benchtop lateral flow readers and visual inspection. The additional benefit of automating and standardizing LFA data collection and aggregation poses a vital improvement for low-resource health facilities and could increase the accuracy and speed of data reporting in surveillance campaigns.

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Evaluating Network Readiness for mHealth Interventions Using the Beacon Mobile Phone App: Application Development and Validation Study

Cited by 6 publications

References 23 publications

Time to act mature—Gearing eHealth evaluations towards technology readiness levels

Time to act mature—Gearing eHealth evaluations towards technology readiness levels

Understanding On-Campus Interactions With a Semiautomated, Barcode-Based Platform to Augment COVID-19 Contact Tracing: App Development and Usage

mHAT app for automated malaria rapid test result analysis and aggregation: a pilot study

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