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BackgroundText messaging (short message service, SMS) has been shown to be effective in delivering interventions for various diseases and health conditions, including smoking cessation. While there are many published studies regarding smoking cessation text messaging interventions, most do not provide details about the study’s operational methods. As a result, there is a gap in our understanding of how best to design studies of smoking cessation text messaging programs.ObjectiveThe purpose of this paper is to detail the operational methods used to conduct a randomized trial comparing three different versions of the National Cancer Institute’s SmokefreeText (SFTXT) program, designed for smokers 18 to 29 years of age. We detail our methods for recruiting participants from the Internet, reducing fraud, conducting online data collection, and retaining panel study participants.MethodsParticipants were recruited through website advertisements and market research online panels. Screening questions established eligibility for the study (eg, 18 to 29 years of age, current smoker). Antifraud measures screened out participants who could not meet the study requirements. After completing a baseline survey, participants were randomized to one of three study arms, which varied by type and timing of text message delivery. The study offered US $20 gift cards as incentives to complete each of four follow-up surveys. Automated email reminders were sent at designated intervals to increase response rates. Researchers also provided telephone reminders to those who had not completed the survey after multiple email reminders. We calculated participation rates across study arms and compared the final sample characteristics to the Current Population Survey to examine generalizability.ResultsRecruitment methods drove 153,936 unique visitors to the SFTXT Study landing page and 27,360 began the screener. Based on the screening questions, 15,462 out of 27,360 responders (56.51%) were eligible to participate. Of the 15,462 who were eligible, 9486 passed the antifraud measures that were implemented; however, 3882 failed to verify their email addresses or cell phone numbers, leaving 5604 who were invited to complete the baseline survey. Of the 5604 who were invited, 4432 completed the baseline survey, but only 4027 were retained for analysis because 405 did not receive the intervention.ConclusionsAlthough antifraud measures helped to catch participants who failed study requirements and could have biased the data collected, it is possible that the email and cell phone verification check excluded some potentially eligible participants from the study. Future research should explore ways to implement verification methods without risking the loss of so many potential participants.ClinicalTrialClinical Trials.gov NCT01885052; https://clinicaltrials.gov/ct2/show/NCT01885052; (Archived by WebCite at http://www.webcitation.org/6iWzcmFdw)

Household scanner data are increasingly used to inform health policy such as sugar-sweetened beverage taxes. This article examines whether differences in the level of reported expenditures between IRI Consumer Network scanner panel and the Consumer Expenditure Survey (CES) lead to important differences in demand elasticities and policy simulation outcomes. Using each dataset, we estimated a structural consumer demand system with seven food groups and a numéraire good. To compare the two datasets on a level playing field, we went to great lengths to ensure that the explanatory variables in the two demand models were comparably constructed. Results indicate that scanner data households are not consistently more price responsive than the general population and underreported Consumer Network expenditures do not seem to result in systematic differences in price elasticities. The income elasticities are uniformly lower in Consumer Network than in CES for higher income households because of the positive association between income and the degree of underreporting. This, however, has limited effects on uncompensated price elasticities and policy simulations because food budget shares are small for higher income households. Overall, these findings support continued use of household scanner data in health policy research related to effects of price (dis)incentives.

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