» Articles » PMID: 26668207

Social Media-based Civic Engagement Solutions for Dengue Prevention in Sri Lanka: Results of Receptivity Assessment

Overview
Journal Health Educ Res
Specialty Medical Education
Date 2015 Dec 16
PMID 26668207
Citations 8
Authors
Affiliations
Soon will be listed here.
Abstract

This article focuses on a novel social media-based system that addresses dengue prevention through an integration of three components: predictive surveillance, civic engagement and health education. The aim was to conduct a potential receptivity assessment of this system among smartphone users in the city of Colombo, the epicenter of the dengue epidemic in the island country of Sri Lanka. Grounded in Protection Motivation Theory (PMT) and using a convenience sampling approach, the cross-sectional survey assessed perceived severity (PSe), perceived susceptibility (PSu), perceived response efficacy (PRE), perceived self-efficacy (PSE) and intention-to-use (IU) among 513 individuals. The overall receptivity to the system was high with a score of >4.00 on a five-point scale. Participants belonging to younger, better educated and higher income groups reported significantly better perceptions of the efficaciousness of the system, were confident in their ability to use the system, and planned to use it in the future. PMT variables contributed significantly to regression models predicting IU. We concluded that a social media-based system for dengue prevention will be positively received among Colombo residents and a targeted, strategic health communication effort to raise dengue-related threat perceptions will be needed to encourage greater adoption and use of the system.

Citing Articles

Assessment of patterns and related factors in using social media platforms to access health and oral health information among Sri Lankan adults, with special emphasis on promoting oral health awareness.

Jayasinghe Y, Kanmodi K, Jayasinghe R, Jayasinghe R BMC Public Health. 2024; 24(1):1472.

PMID: 38824505 PMC: 11143610. DOI: 10.1186/s12889-024-19008-5.


Effectiveness of dengue training programmes on prevention and control among high school students in the Yangon region, Myanmar.

Aung S, Phuanukoonnon S, Kyaw A, Lawpoolsri S, Sriwichai P, Soonthornworasiri N Heliyon. 2023; 9(6):e16759.

PMID: 37292340 PMC: 10245065. DOI: 10.1016/j.heliyon.2023.e16759.


Level of dengue preventive practices and associated factors in a Malaysian residential area during the COVID-19 pandemic: A cross-sectional study.

Mashudi D, Ahmad N, Mohd Said S PLoS One. 2022; 17(4):e0267899.

PMID: 35486657 PMC: 9053802. DOI: 10.1371/journal.pone.0267899.


Social Media-Based Interventions for Health Behavior Change in Low- and Middle-Income Countries: Systematic Review.

Seiler J, Libby T, Jackson E, Lingappa J, Evans W J Med Internet Res. 2022; 24(4):e31889.

PMID: 35436220 PMC: 9052020. DOI: 10.2196/31889.


Smartphone geospatial apps for dengue control, prevention, prediction, and education: MOSapp, DISapp, and the mosquito perception index (MPI).

Babu A, Niehaus E, Shah S, Unnithan C, Ramkumar P, Shah J Environ Monit Assess. 2019; 191(Suppl 2):393.

PMID: 31254076 DOI: 10.1007/s10661-019-7425-0.


References
1.
McNab C . What social media offers to health professionals and citizens. Bull World Health Organ. 2009; 87(8):566. PMC: 2733259. DOI: 10.2471/blt.09.066712. View

2.
Munro S, Lewin S, Swart T, Volmink J . A review of health behaviour theories: how useful are these for developing interventions to promote long-term medication adherence for TB and HIV/AIDS?. BMC Public Health. 2007; 7:104. PMC: 1925084. DOI: 10.1186/1471-2458-7-104. View

3.
Terry M . Twittering healthcare: social media and medicine. Telemed J E Health. 2009; 15(6):507-10. DOI: 10.1089/tmj.2009.9955. View

4.
Chou W, Hunt Y, Beckjord E, Moser R, Hesse B . Social media use in the United States: implications for health communication. J Med Internet Res. 2009; 11(4):e48. PMC: 2802563. DOI: 10.2196/jmir.1249. View

5.
Lazer D, Kennedy R, King G, Vespignani A . Big data. The parable of Google Flu: traps in big data analysis. Science. 2014; 343(6176):1203-5. DOI: 10.1126/science.1248506. View