Evidence map›Paper›PMID 33284126›Full record

ArticleJMIR mHealth and uHealth2020

Effectiveness of a Mobile-Based Influenza-Like Illness Surveillance System (FluMob) Among Health Care Workers: Longitudinal Study.

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 and 2 more

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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

  1. Pooled it
  2. Pooled it
  3. Observational
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
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

12 authors.

May Oo LwinWee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore.ORCID 0000-0003-1832-8242
Jiahui LuSchool of New Media and Communication, Tianjin University, Tianjin, China.ORCID 0000-0003-2984-7435
Anita SheldenkarWee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore.ORCID 0000-0002-5402-9269
Chitra PanchapakesanWee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore.ORCID 0000-0003-3493-8390
Yi-Roe TanNational Centre for Infectious Diseases, Singapore, Singapore.ORCID 0000-0002-4013-7330
Peiling YapThe International Digital Health & Artificial Intelligence Research Collaborative (I-DAIR), Graduate Institute of Geneva, Geneva, Switzerland.ORCID 0000-0002-5964-8210
Mark I ChenNational Centre for Infectious Diseases, Singapore, Singapore.ORCID 0000-0001-9369-5830
Vincent Tk ChowNational University of Singapore, Singapore, Singapore.ORCID 0000-0003-2634-6213
Koh Cheng ThoonKK Women's and Children's Hospital, Singapore, Singapore.ORCID 0000-0002-2844-010X
Chee Fu YungKK Women's and Children's Hospital, Singapore, Singapore.ORCID 0000-0001-9605-7690
Li Wei AngNational Centre for Infectious Diseases, Singapore, Singapore.ORCID 0000-0002-0036-255X
Brenda Sp AngNational Centre for Infectious Diseases, Singapore, Singapore.ORCID 0000-0003-3102-8287

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExisting 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.

objectiveThe 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.

methodsThe 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.

resultsOn 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.

conclusionsMobile-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.

Indexed as

Influenza, HumanChildFemaleHealth PersonnelHumansIncidenceLongitudinal StudiesSingaporehealth care workersinfluenza-like illnessmobile phoneparticipatory surveillancesyndromic surveillance

Identifiers

PMID33284126
PMCPMC7752531

What OpenQuestion holds

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LicenceCC BY
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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.