Forecasting user perceptions of mHealth apps: AI-driven insights from large-scale user-generated content

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...

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Autores: Alzate Barricarte, Miriam, Vidaurreta Apesteguía, Paula, Morales-Garzón, Andrea, Gutiérrez-Batista, Karel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/56120
Acceso en línea:https://hdl.handle.net/2454/56120
Access Level:acceso abierto
Palabra clave:User perceptions
mHealth apps
Consumer behavior
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spelling Forecasting user perceptions of mHealth apps: AI-driven insights from large-scale user-generated contentAlzate Barricarte, MiriamVidaurreta Apesteguía, PaulaMorales-Garzón, AndreaGutiérrez-Batista, KarelUser perceptionsmHealth appsConsumer behaviorUser 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.This work was supported by the Spanish Ministry of Science and Innovation (grant number: TED2021-129513B-C21), by the Public University of Navarre (grant number: PJUPNA2023-11395) and by the Ministry of Economic Transformation, Industry, Knowledge and Universities of the Regional Government of Andalusia, through a pre-doctoral fellowship program (grant number: PREDOC_00298). This work was also supported by the NAIR Center (Navarra Artificial Intelligence Research Center) and by the Government of Navarre, through a research grant under “Programa MRR Investigo”.ElsevierGestión de EmpresasEnpresen KudeaketaUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA2023-11395Gobierno de Navarra / Nafarroako Gobernua2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/56120reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/AEI//TED2021-129513B-C21© 2025 The Authors. This is an open access article under the CC BY license.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/561202026-06-17T12:41:47Z
dc.title.none.fl_str_mv Forecasting user perceptions of mHealth apps: AI-driven insights from large-scale user-generated content
title Forecasting user perceptions of mHealth apps: AI-driven insights from large-scale user-generated content
spellingShingle Forecasting user perceptions of mHealth apps: AI-driven insights from large-scale user-generated content
Alzate Barricarte, Miriam
User perceptions
mHealth apps
Consumer behavior
title_short Forecasting user perceptions of mHealth apps: AI-driven insights from large-scale user-generated content
title_full Forecasting user perceptions of mHealth apps: AI-driven insights from large-scale user-generated content
title_fullStr Forecasting user perceptions of mHealth apps: AI-driven insights from large-scale user-generated content
title_full_unstemmed Forecasting user perceptions of mHealth apps: AI-driven insights from large-scale user-generated content
title_sort Forecasting user perceptions of mHealth apps: AI-driven insights from large-scale user-generated content
dc.creator.none.fl_str_mv Alzate Barricarte, Miriam
Vidaurreta Apesteguía, Paula
Morales-Garzón, Andrea
Gutiérrez-Batista, Karel
author Alzate Barricarte, Miriam
author_facet Alzate Barricarte, Miriam
Vidaurreta Apesteguía, Paula
Morales-Garzón, Andrea
Gutiérrez-Batista, Karel
author_role author
author2 Vidaurreta Apesteguía, Paula
Morales-Garzón, Andrea
Gutiérrez-Batista, Karel
author2_role author
author
author
dc.contributor.none.fl_str_mv Gestión de Empresas
Enpresen Kudeaketa
Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA2023-11395
Gobierno de Navarra / Nafarroako Gobernua
dc.subject.none.fl_str_mv User perceptions
mHealth apps
Consumer behavior
topic User perceptions
mHealth apps
Consumer behavior
description 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.
publishDate 2026
dc.date.none.fl_str_mv 2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/56120
url https://hdl.handle.net/2454/56120
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI//TED2021-129513B-C21
dc.rights.none.fl_str_mv © 2025 The Authors. This is an open access article under the CC BY license.
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2025 The Authors. This is an open access article under the CC BY license.
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
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instname_str Universidad Pública de Navarra
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