ReviewPatient preference and adherence2024
A Review of Sensor-Based Interventions for Supporting Patient Adherence to Inhalation Therapy.
Review in Patient preference and adherence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- From Sensors to Care: How Robotic Skin Is Transforming Modern Healthcare-A Mini Review.Sensors (Basel, Switzerland) · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This review aims to provide a comprehensive overview of sensor technologies employed in interventions to enhance patient adherence to inhalation therapy for chronic respiratory diseases, with a particular emphasis on human factors. Sensor-based interventions offer opportunities to improve adherence through monitoring and feedback; however, a deeper understanding of how these technologies interact with patients is essential. Patients and Methods: We conducted a systematic review by searching online databases, including PubMed, Scopus, Web of Science, Science Direct, and ACM Digital Library, spanning the timeframe from January 2014 to December 2023. Our inclusion criteria focused on studies that employed sensor-based technologies to enhance patient adherence to inhalation therapy. Results: The initial search yielded 1563 results. After a thorough screening process, we selected 37 relevant studies. These sensor-based interventions were organized within a comprehensive HFE framework, including data collection, data processing, system feedback, and system feasibility. The data collection phase comprised person-related, task-related, and physical environment-related data. Various approaches to data processing were employed, encompassing applications for assessing intervention effectiveness, monitoring patient behaviour, and identifying disease risks, while system feedback included reminders and alerts, data visualization, and persuasive features. System feasibility was evaluated based on patient acceptance, usability, and device cost considerations. Conclusion: Sensor-based interventions hold significant promise for improving adherence to inhalation therapy. This review highlights the necessity of an integrated "person-task-physical environment" system to advance future sensor technologies. By capturing comprehensive data on patient health, device usage patterns, and environmental conditions, this approach enables more personalized and effective adherence support. Key recommendations include standardizing data integration protocols, employing advanced algorithms for insights generation, enhancing interactive visual features for accessibility, integrating persuasive design elements to boost engagement, exploring the advantages of conversational agents, and optimizing experience to increase patient acceptance.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.