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Data integrity–based methodology and checklist for identifying implementation risks of physiological sensing in mobile health projects: Quantitative and qualitative analysis

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dc.contributor.author Zhang, J.
dc.contributor.author Tüshaus, L.
dc.contributor.author Martínez, N.N.
dc.contributor.author Moreo, M.
dc.contributor.author Verastegui, H.
dc.contributor.author Hartinger, S.M.
dc.contributor.author Mäusezahl, D.
dc.contributor.author Karlen, W.
dc.date.accessioned 2019-03-05T15:23:27Z
dc.date.available 2019-03-05T15:23:27Z
dc.date.issued 2018
dc.identifier.uri https://hdl.handle.net/20.500.12866/5883
dc.description.abstract Background: Mobile health (mHealth) technologies have the potential to bring health care closer to people with otherwise limited access to adequate health care. However, physiological monitoring using mobile medical sensors is not yet widely used as adding biomedical sensors to mHealth projects inherently introduces new challenges. Thus far, no methodology exists to systematically evaluate these implementation challenges and identify the related risks. Objective: This study aimed to facilitate the implementation of mHealth initiatives with mobile physiological sensing in constrained health systems by developing a methodology to systematically evaluate potential challenges and implementation risks. Methods: We performed a quantitative analysis of physiological data obtained from a randomized household intervention trial that implemented sensor-based mHealth tools (pulse oximetry combined with a respiratory rate assessment app) to monitor health outcomes of 317 children (aged 6-36 months) that were visited weekly by 1 of 9 field workers in a rural Peruvian setting. The analysis focused on data integrity such as data completeness and signal quality. In addition, we performed a qualitative analysis of pretrial usability and semistructured posttrial interviews with a subset of app users (7 field workers and 7 health care center staff members) focusing on data integrity and reasons for loss thereof. Common themes were identified using a content analysis approach. Risk factors of each theme were detailed and then generalized and expanded into a checklist by reviewing 8 mHealth projects from the literature. An expert panel evaluated the checklist during 2 iterations until agreement between the 5 experts was achieved. Results: Pulse oximetry signals were recorded in 78.36% (12,098/15,439) of subject visits where tablets were used. Signal quality decreased for 1 and increased for 7 field workers over time (1 excluded). Usability issues were addressed and the workflow was improved. Users considered the app easy and logical to use. In the qualitative analysis, we constructed a thematic map with the causes of low data integrity. We sorted them into 5 main challenge categories: environment, technology, user skills, user motivation, and subject engagement. The obtained categories were translated into detailed risk factors and presented in the form of an actionable checklist to evaluate possible implementation risks. By visually inspecting the checklist, open issues and sources for potential risks can be easily identified. Conclusions: We developed a data integrity–based methodology to assess the potential challenges and risks of sensor-based mHealth projects. Aiming at improving data integrity, implementers can focus on the evaluation of environment, technology, user skills, user motivation, and subject engagement challenges. We provide a checklist to assist mHealth implementers with a structured evaluation protocol when planning and preparing projects. en_US
dc.language.iso eng
dc.publisher JMIR Publications
dc.relation.ispartof urn:issn:2291-5222
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject MHealth en_US
dc.subject Content analysis en_US
dc.subject Data completeness en_US
dc.subject Data quality en_US
dc.subject Digital health en_US
dc.subject Implementation research en_US
dc.subject Medical sensors en_US
dc.subject Physiological monitoring en_US
dc.subject Signal quality en_US
dc.title Data integrity–based methodology and checklist for identifying implementation risks of physiological sensing in mobile health projects: Quantitative and qualitative analysis en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.2196/11896
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.02.00 es_PE

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