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To map the evidence on patient engagement in mobile technology-based rehabilitation for arthroplasty, including outcome indicators, data collection methods, assessment results, facilitators and barriers, and promoting strategies.
A scoping review.
This study was conducted using a five-stage methodological framework, which included identifying the research questions, identifying relevant studies, selecting the studies, charting the data, and collating, summarising, and reporting the results.
Ten computerised databases were searched to identify eligible studies published between January 2015 and March 2024.
Forty-seven studies were included in this review. Most studies used data on patient adherence to interventions and programme usage to indicate patient engagement in mobile arthroplasty rehabilitation. Data were primarily collected through mobile device records and online or paper-based surveys. Over half of the studies reported a high level of patient engagement in mobile arthroplasty rehabilitation. Patient engagement was influenced by individual and environmental factors, such as the design of programmes, patients' ability to engage with technology, and the accessibility and functionality of equipment. Strategies to promote patient engagement include applying user-centred design principles, offering support from healthcare professionals, caregivers, and peer patients, and employing behaviour-changing strategies.
Existing studies have shown promising results in patient adherence to and use of mobile arthroplasty rehabilitation programmes. Further research can explore engaging patients in programme development, optimising outcome evaluation and data collection, identifying the mechanisms of patient engagement, and testing the effectiveness of promoting strategies.
The study findings provide practical implications for nurses and other healthcare professionals to deepen their understanding of patient engagement in mobile arthroplasty rehabilitation. They may consider employing strategies, such as user-centred design, to enhance patient engagement in mobile rehabilitation programmes, thereby improving patient care.
This review adhered to the PRISMA-ScR checklist.
No patient or public contribution.
Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into four types (neuroischaemic ulcer [NIU], surgical site infections [SSI], venous leg ulcers [VLU], pressure ulcer [PU]), measured with automatic estimation of width, length and depth and segmented into 18 wound and peri-wound features. Data pre-processing was performed using oversampling and augmentation techniques. Convolutional and deep learning models were utilized for model development. The model was evaluated with accuracy, F1 score and receiver operating characteristic (ROC) curves. Explainability methods were used to interpret AI decision reasoning. A web browser application was developed to demonstrate results of the wound AI model with explainability. After development, the model was tested on additional 15 476 unlabelled images to evaluate effectiveness. After the development on the training and validation dataset, the model performance on unseen labelled images in the test set achieved an AUROC of 0.99 for wound classification with mean accuracy of 95.9%. For wound measurements, the model achieved AUROC of 0.97 with mean accuracy of 85.0% for depth classification, and AUROC of 0.92 with mean accuracy of 87.1% for width and length determination. For wound segmentation, an AUROC of 0.95 and mean accuracy of 87.8% was achieved. Testing on unlabelled images, the model confidence score for wound classification was 82.8% with an explainability score of 60.6%. Confidence score was 87.6% for depth classification with 68.0% explainability score, while width and length measurement obtained 93.0% accuracy score with 76.6% explainability. Confidence score for wound segmentation was 83.9%, while explainability was 72.1%. Using explainable AI models, we have developed an algorithm and application for analysis of vascular wound images from an Asian population with accuracy and explainability. With further development, it can be utilized as a clinical decision support system and integrated into existing healthcare electronic systems.
FreshRSS 