Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146144
Title: Effectiveness of a mobile-based influenza-like illness surveillance system (FluMob) among health care workers : longitudinal study
Authors: Lwin, May Oo
Lu, Jiahui
Sheldenkar, Anita
Panchapakesan, Chitra
Tan, Yi-Roe
Yap, Peiling
Chen, Mark I.
Chow, Vincent T. K.
Thoon, Koh Cheng
Yung, Chee Fu
Ang, Li Wei
Ang, Brenda S. P.
Keywords: Visual arts and music::Media
Issue Date: 2020
Source: Lwin, 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:10.2196/19712
Project: CDPHRG13NOV020
Journal: JMIR mHealth and uHealth
Abstract: Background: 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.
URI: https://hdl.handle.net/10356/146144
ISSN: 2291-5222
DOI: 10.2196/19712
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:WKWSCI Journal Articles

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