Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Jun 13, 2025
Open Peer Review Period: Jun 15, 2025 - Aug 10, 2025
Date Accepted: Oct 7, 2025
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Effectiveness of Digital Interventions for Low-Income, Food-Insecure Populations: Natural Language Processing Study of WIC Smartphone App User Reviews, 2013-2024

Lee J

Effectiveness of Digital Interventions for Low-Income, Food-Insecure Populations: Natural Language Processing Study of WIC Smartphone App User Reviews, 2013-2024

J Med Internet Res 2025;27:e78984

DOI: 10.2196/78984

PMID: 41474645

PMCID: 12755294

Effectiveness of Digital Interventions for Low-Income, Food-Insecure Populations: Insights from WIC Smartphone Applications, 2013-2024

  • Jihye Lee

ABSTRACT

Background:

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is a federal nutrition assistance program for low-income, food-insecure mothers and young children in the United States. Despite its intended goals, many eligible individuals forgo WIC benefits, in part due to administrative burden – onerous experiences encountered when navigating public benefits programs. In response, a range of digital interventions and policy waivers were introduced during the COVID-19 pandemic, but their effectiveness in reducing access barriers remains unclear.

Objective:

This study examined the effectiveness of digital interventions for WIC by analyzing user reviews of WIC smartphone apps utilized by local agencies. Specifically, it investigated (a) how obstacles to WIC access manifested in daily interactions with these apps, (b) how user experiences changed after the onset of the COVID-19 pandemic, and (c) how these changes were associated with program satisfaction.

Methods:

An original dataset of user reviews (N = 28,212) was compiled for 26 WIC smartphone apps between 2013 and 2024. Structural topic modeling identified eight key themes in the reviews and assessed changes in user experiences following COVID-19. A mixed-effects analysis was conducted to examine the relationship between identified themes and app ratings.

Results:

The structural topic modeling showed that WIC apps were largely effective in reducing access barriers and improving participation. Pre-COVID-19 reviews most often cited frustrations such as account authentication issues, insufficient customer support, document upload difficulties, and unsuccessful troubleshooting after updates. Although some technical challenges persisted, post-COVID-19 reviews reflected greater appreciation for features that alleviated obstacles, including program tracking, shopping and benefit redemption, and ease of use. Mixed-effects analysis indicated that topics more prominent in post-COVID-19 reviews were significantly associated with increased satisfaction: topics related to program tracking (B = 0.20, SE = 0.06, P = .001), shopping and redemption features (B = 0.18, SE = 0.07, P = .01), and ease of use (B = 0.10, SE = 0.05, P = .04) predicted higher app ratings. In contrast, topics reflecting administrative burden and access obstacles prior to COVID-19 were not significantly associated with app ratings.

Conclusions:

User-centered digital interventions can improve WIC access and participation by reducing administrative burdens and enhancing service delivery.


 Citation

Please cite as:

Lee J

Effectiveness of Digital Interventions for Low-Income, Food-Insecure Populations: Natural Language Processing Study of WIC Smartphone App User Reviews, 2013-2024

J Med Internet Res 2025;27:e78984

DOI: 10.2196/78984

PMID: 41474645

PMCID: 12755294

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.