Sentiment and Thematic Analysis of User Reviews for FDA-Cleared Prescription Digital Therapeutics: A Mixed-Methods Real-World Evidence Study

Background Prescription digital therapeutics (PDTs) are FDA-cleared, evidence-backed, smartphone-based interventions. While clinical trials establish efficacy under controlled conditions, the patient experience in real-world settings remains poorly characterized. App store reviews, though informal, offer a potential source of real-world evidence (RWE) reflecting user sentiment, barriers to engagement, and perceived benefit. Objective To characterize the real-world user experience of FDA-cleared PDTs through a mixed-methods analysis of publicly available app store reviews, combining quantitative sentiment classification with qualitative thematic analysis. Methods FDA-cleared PDTs via FDA's De Novo or 510(k) pathways were identified. Reviews were collected from the Apple App Store and Google Play Store using structured scraping tools. A rules-based sentiment classifier was applied based on user star ratings, and thematic content analysis was conducted using a hybrid natural language processing and manual coding approach. Reviews were grouped by app and platform, and dominant themes were extracted using unsupervised topic modeling. Results Of the 13 PDTs identified, seven had publicly accessible user reviews, yielding a dataset of 247 unique entries: AspyreRx, EndeavorRx, Regulora, Rejoyn, reSET, reSET-O, and Stanza. Sentiment classification revealed that 25.1% of reviews were positive, 12.6% neutral, and 62.3% negative. Thematic analysis identified six recurrent themes, including pediatric benefit and engagement, rewards and incentives, access and activation barriers, technical issues, emotional reactions and cost sensitivity, and boredom and frustration. While negative sentiment was more prevalent overall, several products, particularly Rejoyn and EndeavorRx, received meaningful reports of perceived benefit and therapeutic value. Company transitions were noted to have disrupted the continuity of historical review data of some PDTs. Conclusions App store reviews provide a rich, user-centered source of RWE and insights for PDTs. They highlight dimensions of access, usability, and trust not typically captured in clinical trials. Integrating such feedback into post-market evaluation may inform future design, regulation, and reimbursement of software-based therapeutics.

Authors

Institutions

Publication Details

Journal
Cureus
Published
2025-05-23
DOI
https://doi.org/10.7759/cureus.84710
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sentiment and Thematic Analysis of User Reviews for FDA-Cleared Prescription Digital Therapeutics: A Mixed-Methods Real-World Evidence Study

Shaheen Emmanuel Lakhan
Cureus
Digital Mental Health Interventions
article

Sentiment and Thematic Analysis of User Reviews for FDA-Cleared Prescription Digital Therapeutics: A Mixed-Methods Real-World Evidence Study

Shaheen Emmanuel Lakhan
article en

Abstract

Background Prescription digital therapeutics (PDTs) are FDA-cleared, evidence-backed, smartphone-based interventions. While clinical trials establish efficacy under controlled conditions, the patient experience in real-world settings remains poorly characterized. App store reviews, though informal, offer a potential source of real-world evidence (RWE) reflecting user sentiment, barriers to engagement, and perceived benefit. Objective To characterize the real-world user experience of FDA-cleared PDTs through a mixed-methods analysis of publicly available app store reviews, combining quantitative sentiment classification with qualitative thematic analysis. Methods FDA-cleared PDTs via FDA's De Novo or 510(k) pathways were identified. Reviews were collected from the Apple App Store and Google Play Store using structured scraping tools. A rules-based sentiment classifier was applied based on user star ratings, and thematic content analysis was conducted using a hybrid natural language processing and manual coding approach. Reviews were grouped by app and platform, and dominant themes were extracted using unsupervised topic modeling. Results Of the 13 PDTs identified, seven had publicly accessible user reviews, yielding a dataset of 247 unique entries: AspyreRx, EndeavorRx, Regulora, Rejoyn, reSET, reSET-O, and Stanza. Sentiment classification revealed that 25.1% of reviews were positive, 12.6% neutral, and 62.3% negative. Thematic analysis identified six recurrent themes, including pediatric benefit and engagement, rewards and incentives, access and activation barriers, technical issues, emotional reactions and cost sensitivity, and boredom and frustration. While negative sentiment was more prevalent overall, several products, particularly Rejoyn and EndeavorRx, received meaningful reports of perceived benefit and therapeutic value. Company transitions were noted to have disrupted the continuity of historical review data of some PDTs. Conclusions App store reviews provide a rich, user-centered source of RWE and insights for PDTs. They highlight dimensions of access, usability, and trust not typically captured in clinical trials. Integrating such feedback into post-market evaluation may inform future design, regulation, and reimbursement of software-based therapeutics.

CureusVol. 17(5)
Global Neuroscience Initiative Foundation (US), United Therapeutics (United States) (US), A.T. Still University (US), Morehouse School of Medicine (US), Western University of Health Sciences (US)
Openalex Percentile: Top 87%
Digital Mental Health Interventions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.