Analysis of article using Artificial Intelligence tools
|Author||Boswell M.A.; Kidziński Ł.; Hicks J.L.; Uhlrich S.D.; Falisse A.; Delp S.L.|
|Title||Smartphone videos of the sit-to-stand test predict osteoarthritis and health outcomes in a nationwide study|
Boswell M.A.; Kidziński Ł.; Hicks J.L.; Uhlrich S.D.; Falisse A.; Delp S.L. Smartphone videos of the sit-to-stand test predict osteoarthritis and health outcomes in a nationwide study,npj Digital Medicine 6 1
|Keywords||Diagnosis; Laboratories; mHealth; Motion analysis; Body mass; Health outcomes; Laboratory equipments; Mass index; Mental health; Physical function; Physical health; Sit-to-stand; Smart phones; Stand test; adult; aged; Article; body mass; computer vision; ethnicity; female; health; health care survey; human; kinematics; knee osteoarthritis; major clinical study; male; measurement; mental health; osteoarthritis; outcome assessment; physical activity; quality of life; questionnaire; sit-to-stand test; statistics; treatment outcome; very elderly; Smartphones
|Link to article|| https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149959446&doi=10.1038%2fs41746-023-00775-1&partnerID=40&md5=6f3cec0c819d6f52668039f7bd162141
|Abstract||Physical function decline due to aging or disease can be assessed with quantitative motion analysis, but this currently requires expensive laboratory equipment. We introduce a self-guided quantitative motion analysis of the widely used five-repetition sit-to-stand test using a smartphone. Across 35 US states, 405 participants recorded a video performing the test in their homes. We found that the quantitative movement parameters extracted from the smartphone videos were related to a diagnosis of osteoarthritis, physical and mental health, body mass index, age, and ethnicity and race. Our findings demonstrate that at-home movement analysis goes beyond established clinical metrics to provide objective and inexpensive digital outcome metrics for nationwide studies. © 2023, The Author(s).