• Jing Ma, Xu Sun, and Bingjian Liu.
• Faculty of Science and Engineering, University of Nottingham, Ningbo, People's Republic of China.
• Patient Prefer Adher. 2024 Jan 1; 18: 239724132397-2413.

PurposeThis 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 MethodsWe 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.ResultsThe 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.ConclusionSensor-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.© 2024 Ma et al.

4 keywords...

hide…