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Background Patients often mention distress, anxiety, or claustrophobia related to MRI, resulting in no-shows, disturbances of the workflow, and lasting psychological effects. Patients' experience varies and is moderated by socio-demographic aspects alongside the clinical condition. While qualitative studies help understand individuals' experiences, to date a systematic review and aggregation of MRI individuals' experience is lacking. Purpose To investigate how adult patients experience MRI, and the characterizing factors. Study Type Systematic review with meta-aggregation and meta-synthesis. Population 220 patients' reported experience of adults undergoing clinical MRI and 144 quotes from eight qualitative studies. Assessment Systematic search in PubMed, Scopus, Web of Science, and PsycInfo databases according to the PRISMA guidelines. For quality appraisal, the Joanna Briggs Institute (JBI) tools were used. Convergent segregated approach was undertaken. Data Analysis Participant recruitment, setting of exploration, type of interview, and analysis extracted through Joana Briggs Qualitative Assessment and Review Instrument (JBI QARI) tool. Meta-synthesis was supported by a concept map. For meta-aggregation, direct patient quotes were extracted, findings grouped, themes and characterizing factors at each stage abstracted, and categories coded in two cycles. Frequency of statements was quantified. Interviews' raw data unavailability impeded computer-aided analysis. Results Eight articles out of 12,755 initial studies, 220 patients, were included. Meta-aggregation of 144 patient quotes answered: (1) experiences before, at the scanning table, during, and after an MRI, (2) differences based on clinical condition, and (3) characterizing factors, including coping strategies, look-and-feel of medical technology, interaction with professionals, and information. Seven publications lack participants' health literacy level, occupation, and eight studies lack developmental conditions, ethnicity, or country of origin. Six studies were conducted in university hospitals. Data Conclusion Aggregation of patients' quotes provide a foundational description of adult patients' MRI experience across the stages of an MRI process. Insufficient raw data of individual quotes and limited socio-demographic diversity may constrain the understanding of individual experience and characterizing factors. Level of Evidence 1 Technical Efficacy Stage 5

 

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We validate the OpenSense framework for IMU-based joint angle estimation and furthermore analyze the framework's ability for sensor selection and optimal positioning during activities of daily living (ADL). Personalized musculoskeletal models were created from anthropometric data of 19 participants. Quaternion coordinates were derived from measured IMU data and served as input to the simulation framework. Six ADLs, involving upper and lower limbs were measured and a total of 26 angles analyzed. We compared the joint kinematics of IMU-based simulations with those of optical marker-based simulations for most important angles per ADL. Additionally, we analyze the influence of sensor count on estimation performance and deviations between joint angles, and derive the best sensor combinations. We report differences in functional range of motion (fRoMD) estimation performance. Results for IMU-based simulations showed MAD, RMSE, and fRoMD of 4.8°, 6.6°, 7.2° for lower limbs and for lower limbs and 9.2°, 11.4°, 13.8° for upper limbs depending on the ADL. Overall, sagittal plane movements (flexion/extension) showed lower median MAD, RMSE, and fRoMD compared to transversal and frontal plane movements (rotations, adduction/abduction). Analysis of sensor selection showed that after three sensors for the lower limbs and four sensors for the complex shoulder joint, the estimation error decreased only marginally. Global optimum (lowest RMSE) was obtained for five to eight sensors depending on the joint angle across all ADLs. The sensor combinations with the minimum count were a subset of the most frequent sensor combinations within a narrowed search space of the 5% lowest error range across all ADLs and participants. Smallest errors were on average < 2° over all joint angles. Our results showed that the open-source OpenSense framework not only serves as a valid tool for realistic representation of joint kinematics and fRoM, but also yields valid results for IMU sensor selection for a comprehensive set of ADLs involving upper and lower limbs. The results can help researchers to determine appropriate sensor positions and sensor configurations without the need for detailed biomechanical knowledge.

 

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