Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146144
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dc.contributor.authorLwin, May Ooen_US
dc.contributor.authorLu, Jiahuien_US
dc.contributor.authorSheldenkar, Anitaen_US
dc.contributor.authorPanchapakesan, Chitraen_US
dc.contributor.authorTan, Yi-Roeen_US
dc.contributor.authorYap, Peilingen_US
dc.contributor.authorChen, Mark I.en_US
dc.contributor.authorChow, Vincent T. K.en_US
dc.contributor.authorThoon, Koh Chengen_US
dc.contributor.authorYung, Chee Fuen_US
dc.contributor.authorAng, Li Weien_US
dc.contributor.authorAng, Brenda S. P.en_US
dc.date.accessioned2021-01-28T03:06:56Z-
dc.date.available2021-01-28T03:06:56Z-
dc.date.issued2020-
dc.identifier.citationLwin, M. O., Lu, J., Sheldenkar, A., Panchapakesan, C., Tan, Y.-R., Yap, P., . . . Ang, B. S. P. (2020). Effectiveness of a mobile-based influenza-like illness surveillance system (FluMob) among health care workers : longitudinal study. JMIR mHealth and uHealth, 8(12), e19712-. doi:10.2196/19712en_US
dc.identifier.issn2291-5222en_US
dc.identifier.urihttps://hdl.handle.net/10356/146144-
dc.description.abstractBackground: Existing studies have suggested that internet-based participatory surveillance systems are a valid sentinel for influenza-like illness (ILI) surveillance. However, there is limited scientific knowledge on the effectiveness of mobile-based ILI surveillance systems. Previous studies also adopted a passive surveillance approach and have not fully investigated the effectiveness of the systems and their determinants. Objective: The aim of this study was to assess the efficiency of a mobile-based surveillance system of ILI, termed FluMob, among health care workers using a targeted surveillance approach. Specifically, this study evaluated the effectiveness of the system for ILI surveillance pertaining to its participation engagement and surveillance power. In addition, we aimed to identify the factors that can moderate the effectiveness of the system. Methods: The FluMob system was launched in two large hospitals in Singapore from April 2016 to March 2018. A total of 690 clinical and nonclinical hospital staff participated in the study for 18 months and were prompted via app notifications to submit a survey listing 18 acute respiratory symptoms (eg, fever, cough, sore throat) on a weekly basis. There was a period of study disruption due to maintenance of the system and the end of the participation incentive between May and July of 2017. Results: On average, the individual submission rate was 41.4% (SD 24.3%), with a rate of 51.8% (SD 26.4%) before the study disruption and of 21.5% (SD 30.6%) after the disruption. Multivariable regression analysis showed that the adjusted individual submission rates were higher for participants who were older (<30 years, 31.4% vs 31-40 years, 40.2% [P<.001]; 41-50 years, 46.0% [P<.001]; >50 years, 39.9% [P=.01]), ethnic Chinese (Chinese, 44.4% vs non-Chinese, 34.7%; P<.001), and vaccinated against flu in the past year (vaccinated, 44.6% vs nonvaccinated, 34.4%; P<.001). In addition, the weekly ILI incidence was 1.07% on average. The Pearson correlation coefficient between ILI incidence estimated by FluMob and that reported by Singapore Ministry of Health was 0.04 (P=.75) with all data and was 0.38 (P=.006) including only data collected before the study disruption. Health care workers with higher risks of ILI and influenza such as women, non-Chinese, allied health staff, those who had children in their households, not vaccinated against influenza, and reported allergy demonstrated higher surveillance correlations. Conclusions: Mobile-based ILI surveillance systems among health care workers can be effective. However, proper operation of the mobile system without major disruptions is vital for the engagement of participants and the persistence of surveillance power. Moreover, the effectiveness of the mobile surveillance system can be moderated by participants’ characteristics, which highlights the importance of targeted disease surveillance that can reduce the cost of recruitment and engagement.en_US
dc.description.sponsorshipMinistry of Health (MOH)en_US
dc.language.isoenen_US
dc.relationCDPHRG13NOV020en_US
dc.relation.ispartofJMIR mHealth and uHealthen_US
dc.rights© May Oo Lwin, Jiahui Lu, Anita Sheldenkar, Chitra Panchapakesan, Yi-Roe Tan, Peiling Yap, Mark I Chen, Vincent TK Chow, Koh Cheng Thoon, Chee Fu Yung, Li Wei Ang, Brenda SP Ang. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 07.12.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.en_US
dc.subjectVisual arts and music::Mediaen_US
dc.titleEffectiveness of a mobile-based influenza-like illness surveillance system (FluMob) among health care workers : longitudinal studyen_US
dc.typeJournal Articleen
dc.contributor.schoolWee Kim Wee School of Communication and Informationen_US
dc.identifier.doi10.2196/19712-
dc.description.versionPublished versionen_US
dc.identifier.pmid33284126-
dc.identifier.scopus2-s2.0-85097575676-
dc.identifier.issue12en_US
dc.identifier.volume8en_US
dc.subject.keywordsParticipatory Surveillanceen_US
dc.subject.keywordsSyndromic Surveillanceen_US
dc.description.acknowledgementThis research was supported by the Singapore MOH’s National Medical Research Council under its Communicable Diseases—Public Health Research Grant (CDPHRG13NOV020). The authors would like to acknowledge the contribution of broader team members, Huarong Xu and Jie Chen, who helped with data collection in the hospitals.en_US
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