Abstract
BackgroundCustomer intercept interviews are increasingly used to characterize food purchases at retail food outlets and restaurants; however, methodological procedures, logistical issues and response rates using intercept methods are not well described in the food environment literature. The aims of this manuscript were to 1) describe the development and implementation of a customer intercept interview protocol in a large, NIH-funded study assessing food purchases in small and midsize food retailers in Minneapolis and St. Paul, Minnesota, 2) describe intercept interview response rates by store type and environmental factors (e.g., neighborhood socioeconomic status, day/time, weather), and 3) compare demographic characteristics (e.g., gender, race/ethnicity) of participants versus non-participants.MethodsAfter a pilot phase involving 28 stores, a total of 616 interviews were collected from customers exiting 128 stores in fall 2014. The number of eligible customers encountered per hour (a measure of store traffic), participants successfully recruited per hour, and response rates were calculated overall and by store type, neighborhood socio-economic status, day and time of data collection, and weather. Response rates by store type, neighborhood socio-economic status, time and day of data collection, and weather, and characteristics of participants and non-participants were compared using chi-square tests.ResultsThe overall response rate was 35 %, with significantly higher response rates at corner/small grocery stores (47 %) and dollar stores (46 %) compared to food-gas marts (32 %) and pharmacies (26 %), and for data collection between 4:00–6:00 pm on weekdays (40 %) compared to weekends (32 %). The distribution of race/ethnicity, but not gender, differed between participants and non-participants (p < 0.01), with greater participation rates among those identified as Black versus White.ConclusionsCustomer intercept interviews can be successfully used to recruit diverse samples of customers at small and midsize food retailers. Future community-based studies using customer intercept interviews should collect data sufficient to report response rates and consider potential differences between the racial/ethnic composition of the recruited sample and the target population.
Highlights
Customer intercept interviews are increasingly used to characterize food purchases at retail food outlets and restaurants; methodological procedures, logistical issues and response rates using intercept methods are not well described in the food environment literature
The number of eligible customers encountered per hour, participants successfully recruited per hour, and response rates were calculated overall and by store type, neighborhood Socioeconomic status (SES), day and time of data collection, and weather
Participants recruited during a single store visit ranged from 0 to 15
Summary
Customer intercept interviews are increasingly used to characterize food purchases at retail food outlets and restaurants; methodological procedures, logistical issues and response rates using intercept methods are not well described in the food environment literature. Customer intercept (CI) interviews are an increasingly common data collection method for the study of obesity-related policies and programs They have been used to characterize retail food/beverage purchases at small food stores [1,2,3,4] and measure the impact of healthy corner store interventions [5, 6], menu calorie labeling in fast food restaurants [7,8,9,10], and food labeling and taxation experiments in a hospital cafeteria [11]. Reported response rates were 55.2 % outside fast food chain restaurants [10], 32.9 % outside corner stores across all areas of the city [12], and 53 % at baseline and 63 % at follow-up outside corner stores in high poverty neighborhoods [3] None of these studies collected information on non-participants, such as gender or race/ethnicity, to evaluate potential non-response bias in the recruited sample. No prior studies examined how recruitment rates varied by factors such as time of day or neighborhood socioeconomic status
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