Login / Signup

A Case Study of an SMS Text Message Community Panel Survey and Its Potential for Use During the COVID-19 Pandemic.

Lilian ChanNouhad El-HaddadFreeman BeckyBlythe Jane O'HaraLisa WoodlandBen F Harris-Roxas
Published in: JMIR formative research (2021)
During the COVID-19 pandemic many traditional methods of data collection, such as intercept surveys or focus groups, are not feasible. This paper proposes that establishing community panels through SMS text messages may be a useful method during the pandemic, by describing a case study of how an innovative SMS text message community panel was used for the "Shisha No Thanks" project to collect data from young adults of Arabic-speaking background about their attitudes on the harms of waterpipe smoking. Participants were asked to complete an initial recruitment survey, and then subsequently sent 1 survey question per week. The study recruited 133 participants to the SMS text message community panel and the mean response rate for each question was 73.0% (97.1/133) (range 76/133 [57.1%] to 112/133 [84.2%]). The SMS text message community panel approach is not suited for all populations, nor for all types of inquiry, particularly due to limitations of the type of responses that it allows and the required access to mobile devices. However, it is a rapid method for data collection, and therefore during the COVID-19 pandemic, it can provide service providers and policymakers with timely information to inform public health responses. In addition, this method negates the need for in-person interactions and allows for longitudinal data collection. It may be useful in supplementing other community needs assessment activities, and may be particularly relevant for people who are considered to be more difficult to reach, particularly young people, culturally and linguistically diverse communities, and other groups that might otherwise be missed by traditional methods.
Keyphrases
  • mental health
  • healthcare
  • smoking cessation
  • public health
  • cross sectional
  • young adults
  • electronic health record
  • big data
  • sars cov
  • clinical trial
  • machine learning
  • social media
  • data analysis