Designing a WIC App to Improve Health Behaviors: A Latent Class Analysis
By Crixell S, Markides B, Biediger-Friedman L, Reat A, Bishop N
Background: Smartphone apps have potential to effectively deliver health education and improve health behaviors among at-risk populations. To be successful, apps should include user input during stages of development. Previously, a prototype app designed for participants in the Texas Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) was developed based on input from focus groups.
Aims: This research aimed to continue app design by soliciting user input via a survey from a state-wide sample.
Methods: Texas WIC clients were asked about physical activity, healthy eating, and breastfeeding behaviors, stage of change regarding health behaviors, current use of health-related apps, and perceptions of app prototype features. Latent class analysis (n=942) was used to identify mutually exclusive groups based on the strength of participants’ agreement that prototype features would help them exercise more or consume more fruits and vegetables. Logistic regression examined health related characteristics and sociodemographic differences between classes.
Results: Response to app prototype features was positive. A 2-class model best described latent classes. Class members that strongly agreed that prototype features would help them improve health behaviors were younger (< 35 years), not pregnant, already using health-related apps, and in the contemplation, preparation, or action stages of change regarding physical activity.
Conclusion: Refinement of the Texas WIC app should incorporate input from individuals who are pregnant, older than 35 years, or in pre-contemplation regarding physical activity. The iterative process of user-centered design applied in this research may serve as a useful framework for development of other public health apps.
February 11, 2019
Crixell S, Markides B, Biediger-Friedman L, Reat A, Bishop N (2018) Designing a WIC App to Improve Health Behaviors: A Latent Class Analysis. Journal o fMobile Technology in Medicine: Vol. 7, Issue 2, pp 7-16. Available online: http://articles.journalmtm.com/jmtm.7.2.2.pdf