Background: Interactive videogames, virtual reality, and robotics represent a new opportunity for multimodal treatments in many rehabilitation contexts. However, several commercial videogames are designed for leisure and are not oriented toward definite rehabilitation goals. Among the many, Playball (Playwork, Alon 10, Ness Ziona, Israel) is a therapeutic ball that measures both movement and pressure applied on it while performing rehabilitation games. This study aimed: (i) to evaluate whether the use of this novel digital therapy gaming system was clinically effective during shoulder rehabilitation; (ii) to understand whether this gaming rehabilitation program was effective in improving patients' engagement (perceived enjoyment and self-efficacy during therapy; attitude and intention to train at home) in comparison to a control non-gaming rehabilitation program.
Methods: A randomized controlled experimental design was outlined. Twenty-two adults with shoulder pathologies were recruited for a rehabilitation program of ten consecutive sessions. A control (CTRL; N = 11; age: 62.0 ± 10.9 yrs) and an intervention group (PG; N = 11; age: 59.9 ± 10.2 yrs) followed a non-digital and a digital therapy, respectively. The day before (T) and after (T) the rehabilitation program, pain, strength, and mobility assessments were performed, together with six questionnaires: PENN shoulder Score, PACES-short, Self-efficacy, Attitudes to train at home, Intention to train at home, and System usability scale (SUS).
Results: MANOVA analysis showed significant improvements in pain (p < 0.01), strength (p < 0.05), and PENN Shoulder Score (p < 0.001) in both groups. Similarly, patients' engagement improved, with significant increments in Self-efficacy (p < 0.05) and attitude (p < 0.05) scores in both groups after the rehabilitation. Pearson correlation showed significant correlations of the Δ scores (T - T) between PACES and Self-efficacy (r = 0.623; p = 0.041) and between PACES and Intention to train at home (r = 0.674; p = 0.023) only in the PG. SUS score after the rehabilitation (74.54 ± 15.60) overcame the cut-off value of 68, representative of good usability of a device.
Conclusions: The investigated digital therapy resulted as effective as an equivalent non-digital therapy in shoulder rehabilitation. The reported positive relationship between the subject's enjoyment during digital therapy and intention to train at home suggests promising results in possible patient's exercise engagement at home after the rehabilitation in the medical center.
Retrospectively Registered: NCT05230056.
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http://dx.doi.org/10.1186/s12984-023-01188-7 | DOI Listing |
Rheumatol Ther
January 2025
Department of Internal Medicine 3, Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.
Introduction: Prescribable digital health applications (DiGAs) present scalable solutions to improve patient self-management in rheumatology, however real-world evidence is scarce. Therefore, we aimed to assess the effectiveness, usage, and usability of DiGAs prescribed by rheumatologists, as well as patient satisfaction.
Methods: The DiGAReal registry includes adult patients with rheumatic conditions who received a DiGA prescription.
Radiology
January 2025
From the Department of Radiology and Research Institute of Radiology (Y.A., S.M.L., J.C., K.H.D., J.B.S.) and Department of Cardiothoracic Surgery (S.H.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea.
Background The ninth edition of the TNM classification for lung cancer revised the N2 categorization, improving patient stratification, but prognostic heterogeneity remains for the N1 category. Purpose To define the optimal size cutoff for a bulky lymph node (LN) on CT scans and to evaluate the prognostic value of bulky LN in the clinical N staging of lung cancer. Materials and Methods This retrospective study analyzed patients who underwent lobectomy or pneumonectomy for lung cancer between January 2013 and December 2021, divided into development (2016-2021) and validation (2013-2015) cohorts.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Radiology, University Hospital Halle, Ernst-Grube-Strasse 40, 06120 Halle (Saale), Germany (D.S., J.S., J.M.B.); Department of Nuclear Medicine, University of Leipzig, Leipzig, Germany (L.K., T.W.G., R.K.); Diagnostic Imaging and Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI (K.M.M.); Department of Pediatric Radiology, Imaging and Radiation Oncology, Core-Rhode Island, Providence, RI (K.M.M.); Department of Oncology, St Jude Children's Research Hospital, Memphis, Tenn (J.E.F.); Department of Pediatric Hematology and Oncology, University Hospital Giessen-Marburg, Giessen, Germany (C.M.K., D.K.); Medical Faculty of the Martin Luther University of Halle-Wittenberg, Halle (Saale) Germany (C.M.K.); Department of Radiology, University of Wisconsin-Madison, Madison, Wis (S.Y.C.); Roswell Park Comprehensive Cancer Center, Buffalo, NY (K.M.K.); Department of Radiation Oncology, Medical Faculty of the Martin-Luther-University, Halle (Saale), Germany (T.P., D.V.); Department of Radiation Oncology, Mayo Clinic-Jacksonville, Jacksonville, Fla (B.S.H.); Department of Radio-Oncology, Medical University Vienna, Vienna, Austria (K.D.); and Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Mass (S.D.V.).
Staging of pediatric Hodgkin lymphoma is currently based on the Ann Arbor classification, incorporating the Cotswold modifications and the Lugano classification. The Cotswold modifications provide guidelines for the use of CT and MRI. The Lugano classification emphasizes the importance of CT and PET/CT in evaluating both Hodgkin lymphoma and non-Hodgkin lymphoma but focuses on adult patients.
View Article and Find Full Text PDFRadiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
View Article and Find Full Text PDFSwiss Med Wkly
December 2024
Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
In 2015, around 4400 individuals received a diagnosis of lung cancer, and Switzerland recorded approximately 3200 deaths related to lung cancer. Advances in detection, such as lung cancer screening and improved treatments, have led to increased identification of early-stage lung cancer and higher chances of long-term survival. This progress has introduced new considerations in imaging, emphasising non-invasive diagnosis and characterisation techniques like radiomics.
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