Using online crowdsourcing to understand young adult attitudes toward expert-authored messages aimed at reducing hazardous alcohol consumption and to collect peer-authored messages

Abstract

Text message delivered prevention interventions have the potential to improve health behaviors on a large scale, including reducing hazardous alcohol consumption in young adults. Online crowdsourcing can be used to efficiently develop relevant messages, but remains largely understudied. This study aims to use online crowdsourcing to evaluate young adult attitudes toward expert-authored messages and to collect peer-authored messages. We designed an online survey with four drinking scenarios and a demographic questionnaire. We made it available to people who reported age 18–25 years, residence in the US, and any lifetime alcohol consumption via the Amazon Mechanical Turk crowdsourcing platform. Participants rated 71 sample text messages on instrumental (helpful) and affective (interesting) attitude scales and generated their own messages. All messages were coded as informational, motivational, or strategy facilitating. We examined differences in attitudes by message type and by drinking status and sex. We surveyed 272 participants in 48 h, and 222 were included in analysis for a total participant payment cost of $178. Sample mean age was 23 years old, with 50 % being female, 65 % being of white race, and 78 % scored as hazardous drinkers. Informational messages were rated the most helpful, whereas motivational messages were rated the most interesting. Hazardous drinkers rated informational messages less helpful than non-hazardous drinkers. Men reported messages less helpful and interesting than women for most categories. Young adults authored 161 messages, with the highest proportion being motivational. Young adults had variable instrumental and affective attitudes toward expert-authored messages. They generated a substantial number of peer-authored messages that could enhance relevance of future alcohol prevention interventions.