Feasibility study and determination of prerequisites of telecare programme to enhance patient management in lung transplantation: a qualitative study from the perspective of Iranian healthcare providers

Perceptions of barriers to and facilitators of exercise rehabilitation in adults with lung transplantation: a qualitative study in China

BACKGROUND

Exercise is crucial for pulmonary rehabilitation and improving the prognosis of lung transplantation (LTx) patients. However, many LTx patients in China have low exercise tolerance and compliance, and the reasons behind these challenges have not been fully elucidated. Therefore, this qualitative research aims to identify the barriers to and facilitators of exercise rehabilitation in LTx patients.

METHODS

From January to July 2023, 15 stable LTx patients were recruited and participated in in-depth, semi-structured, face-to-face interviews at Henan Provincial People's Hospital. The interview transcripts were analyzed using the COM-B model and the Theoretical Domains Framework (TDF).

RESULTS

Six general themes including 19 barriers and 14 facilitators for the exercise rehabilitation of LTx patients were identified based on the COM-B model and TDF. The barriers to exercise included physical limitations, insufficient exercise endurance, lack of knowledge, and lack of motivation. The facilitators of exercise included motivation, self-efficacy, perceived significance of exercise rehabilitation, and social support.

CONCLUSION

The study offers detailed insight into the development and implementation of exercise rehabilitation intervention strategies for LTx patients. By combining COM-B model and TDF, the study provides strong evidence that active behavior change strategies are required for LTx patients to promote their participation in exercise rehabilitation. Professional support, pulmonary rehabilitation training, behavior change technology, and digital health tools are essential for strengthening the evidence system for reporting exercise efficacy and effectiveness.

Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow

BACKGROUND

Accurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates.

METHOD

An iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design.

RESULTS

According to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7).

CONCLUSION

Through a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.