ReviewJMIR rehabilitation and assistive technologies2025
How AI-Based Digital Rehabilitation Improves End-User Adherence: Rapid Review.
Review in JMIR rehabilitation and assistive technologies, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Artificial intelligence in postural management: a critical review of detection, correction, and clinical applicability.Journal of orthopaedic surgery and research · 2026Review
- Evaluation of the Efficacy of an Artificial Intelligence-Based Assessment and Correction System in the Rehabilitation of Patients Following Anterior Cruciate Ligament Reconstruction Surgery.Journal of clinical medicine · 2026Article
- Exercise-Based Mechanotherapy: From Biomechanical Principles and Mechanotransduction to Precision Regenerative Rehabilitation.International journal of molecular sciences · 2026Review
- The Role of Artificial Intelligence in Exercise-Based Cardiovascular Health Interventions: A Scoping Review.Journal of functional morphology and kinesiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The integration of artificial intelligence (AI) in rehabilitation technology is transforming traditional methods, focusing on personalization and improved outcomes. The growing area of AI in digital rehabilitation (DR) emphasizes the critical role of end-user compliance with rehabilitation programs. Analyzing how AI-driven DR tools can boost this compliance is vital for creating sustainable practices and tackling future challenges. Objective: This study seeks to assess how AI-based DR can improve the end-user compliance or adherence to rehabilitation. Methods: Following the updated recommendations for the Cochrane rapid review methods guidance and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic literature search strategy was led in PubMed, which yielded 922 records, resulting in 6 papers included in this study. Results: The reviewed studies identified 6 key ways in which AI enhances end-user compliance in rehabilitation. The most prevalent method (in 4 studies) involves motivating and engaging users through features like exercise tracking and motivational content. The second method, also noted in 4 studies, focuses on improving communication and information exchange between health care providers and users. Personalized solutions tailored to individual cognitive styles and attitudes were highlighted in 3 studies. Ease of use and system usability, affecting user acceptability, emerged in 2 studies. Additionally, daily notifications, alerts, and reminders were identified as strategies to promote compliance, also noted in 2 studies. While 5 studies looked at AI's role in improving adherence, 1 study specifically assessed AI's capability for objective compliance measurement, contrasting it with traditional subjective self-reports. Conclusions: Our results could be especially relevant and beneficial for rethinking rehabilitation practices and devising effective strategies for the integration of AI in the rehabilitation field, aimed at enhancing end-user adherence to the rehabilitation regimen.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.