Forecasting user perceptions of mHealth apps

AI-driven insights from large-scale user-generated content

Bibliographic Data

ID21403188
AuthorsMiriam Alzate (0000-0003-4414-3030, Universidad Pública de Navarra (UPNA), corresponding author), Paula Vidaurreta-Apesteguia (Universidad de Navarra), Andrea Morales-Garzón (0000-0002-3458-0694, Universidad de Granada), Karel Gutiérrez-Batista, Karel Gutiérrez‐Batista (0000-0003-2711-4625, Universidad de Granada)
Year2026
Volume223
Pages124427
Publication date2026-02-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueTechnological Forecasting and Social Change (JOURNAL)
Journal identifiersISSN: 0040-1625 • E-ISSN: 1873-5509
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.techfore.2025.124427
OpenAlexW4416729670
LanguageEN
References cited66

User perceptions of mHealth apps are critical for forecasting adoption trends, optimizing app design, and evaluating their broader societal implications for public health and digital inclusion. Understanding how users engage with these applications is essential for their sustained use. This research incorporates AI-driven methodologies to systematically analyze large-scale user-generated content (UGC), providing predictive insights into consumer behavior and digital health engagement. Through three interconnected stages, this paper contributes to technological forecasting, digital health management, and marketing analytics by applying Natural Language Processing (NLP) and Large Language Models (LLMs) to classify brand associations in mHealth app reviews. At the first stage, 849,918 reviews from the most downloaded mHealth apps in the US were analyzed and categorized into tracking, nutrition, step counters, and rest/meditation apps. Using BERT-based topic modeling (BERTopic) and KMeans clustering, we classify key topics under Keller's brand association dimensions. At a second stage, a predictive classification model was developed using fine-tuned DistilBERT. At a third stage, an ANOVA analysis was used to examine differences in user attitudes based on brand associations and app type. Findings highlight the high number of product-related attributes mentioned in user conversations. However, emotional benefits are those driving higher user satisfaction with mHealth apps. • NLP and LLM methods can help to uncover brand image associations in mHealth app reviews. • BERTopic modeling was used to extract and classify user reviews into Keller's brand image dimensions. • Emotional benefits influence mHealth adoption most, followed by functional attributes. • Rest apps lead in satisfaction, offering strong functional and emotional engagement. • Insights guide developers and policymakers in optimizing mHealth app engagement

Analytics · Digital content · Digital health · mHealth · Perception · Topic model · User satisfaction · Variance (accounting) · Digital Mental Health Interventions · Innovative Human-Technology Interaction · Mobile Health and mHealth Applications

Tools

Open DOIOpen Access