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10 results on '"Lim, Lilian"'

1. Relationship of Fatigue, Pain Interference, and Physical Disability in Children Newly Diagnosed With Juvenile Idiopathic Arthritis

Author

Choong, Naomi, Batthish, Michelle, Berard, Roberta A., Chédeville, Gaëlle, Feldman, Brian M., Houghton, Kristin M., Huber, Adam M., James, Sarah, Proulx‐Gauthier, Jean‐Philippe, Rumsey, Dax G., Schmeling, Heinrike, Toupin‐April, Karine, Guzman, Jaime, Cabral, David A., Chédeville, Gaëlle, Duffy, Ciarán M., Gerhold, Kerstin, Guzman, Jaime, Hiraki, Linda, Huber, Adam M., Schmeling, Heinrike, Shiff, Natalie J., Tucker, Lori B., Barsalou, Julie, Basodan, Daniah, Batthish, Michelle, Berard, Roberta A., Blanchette, Nicholas, Boire, Gilles, Bolaria, Roxana, Bruns, Alessandra, Cabral, David, Campillo, Sarah, Cellucci, Tania, Chan, Mercedes, Chédeville, Gaëlle, Chhabra, Amieleena, Couture, Julie, Dancey, Paul, De Bruycker, Jean‐Jacques, Demirkaya, Erkan, Dhalla, Muhammed, Duffy, Ciarán M., El Tal, Talal, Gerschman, Tommy, Guzman, Jaime, Heale, Liane, Herrington, Julie, Houghton, Kristin M., Huber, Adan M., Human, Andrea, Johnson, Nicole, Jurencak, Roman, Lang, Bianca, LeBlanc, Claire, Lim, Lilian, Lim, Lily S. H., Luca, Nadia, McColl, Jeanine, McGrath, Tara, McMillan, Tamara, McPherson, Megan, Miettunen, Paivi, Morishita, Kimberly, Ng, Honyan, Park, Jonathan, Proulx‐Gauthier, Jean‐Philippe, Roth, Johannes, Rozenblyum, Evelyn, Rumsey, Dax G., Schmeling, Heinrike, Scuccimarri, Rosie, Soon, Gordon, Stevenson, Rebeka, Stringer, Elizabeth, Tam, Herman, Tucker, Lori B., Twilt, Marinka, Verstegen, Ruud H. J., and Duffy, Karen Watanabe

Abstract

Our objectives were to quantify the relationships among fatigue, pain interference, and physical disability in children with juvenile idiopathic arthritis (JIA) and to test whether fatigue mediates the relationship between pain interference and physical disability in JIA. Patients enrolled within three months of JIA diagnosis in the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry between February 2017 and May 2023 were included. Their parents completed the Patient‐Reported Outcomes Measurement Information System fatigue and pain interference short proxy questionnaires and the Childhood Health Assessment Questionnaire disability index at registry enrollment. Associations were assessed using Pearson correlations and multiple linear regression. Structural equation modeling (SEM) was used to test if fatigue mediates the relationship between pain interference and physical disability. Among 855 patients (61.4% female, 44.1% with oligoarthritis), most reported fatigue and pain interference scores similar to those in the reference population, but 15.6% reported severe fatigue and 7.3% reported severe pain interference, with wide variation across JIA categories. Fatigue was strongly correlated with pain interference (r = 0.72, P< 0.001) and with physical disability (r= 0.60, P< 0.001). Pain interference (β = 0.027, P< 0.001) and fatigue (β = 0.013, P< 0.001) were both associated with physical disability after controlling for each other and potential confounders. SEM supported our hypothesis that fatigue partially mediates the relationship between pain interference and physical disability. Our findings suggest both fatigue and pain interference are independently associated with physical disability in children newly diagnosed with JIA, and the effect of pain interference may be partly mediated by fatigue.

Published
2024
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9. Abstract T P118.

Author

Chen, Zhenzhen, Eng, Jia Yen, Lin, Yiwei, Ong, Siew Seang, Lim, Lilian, Lui, Eunice, Ee, Jie Min, and Chan, Bernard Poon Lap

Published
2014

10. Deep-Learning Markerless Tracking of Infant General Movements using Standard Video Recordings.

Author

Abbasi H, Mollet SR, Williams SA, Lim L, Battin MR, Besier TF, and McMorland AJC

Subjects
Infant, Humans, Male, Movement, Image Processing, Computer-Assisted, Video Recording, Deep Learning, Cerebral Palsy
Abstract

Monitoring spontaneous General Movements (GM) of infants 6-20 weeks post-term age is a reliable tool to assess the quality of neurodevelopment in early infancy. Abnormal or absent GMs are reliable prognostic indicators of whether an infant is at risk of developing neurological impairments and disorders such as cerebral palsy (CP). Therapeutic interventions are most effective at improving neuromuscular outcomes if administered in early infancy. Current clinical protocols require trained assessors to rate videos of infant movements, a time-intensive task. This work proposes a simple, inexpensive, and broadly applicable markerless pose-estimation approach for automatic infant movement tracking using conventional video recordings from handheld devices (e.g., tablets and mobile phones). We leverage the enhanced capabilities of deep-learning technology in image processing to identify 12 anatomical locations (3 per limb) in each video frame, tracking a baby's natural movement throughout the recordings. We validate the capability of resnet152 and a mobile-net-v2-1 to identify body-parts in unseen frames from a full-term male infant, using a novel automatic unsupervised approach that fuses likelihood outputs of a Kalman filter and the deep-nets. Both deep-net models were found to perform very well in the identification of anatomical locations in the unseen data with high average Percentage of Correct Keypoints (aPCK) performances of >99.65% across all locations.Clinical relevance-Results of this research confirm the feasibility of a low-cost and publicly accessible technology to automatically track infants' GMs and diagnose those at higher risk of developing neurological conditions early, when clinical interventions are most effective.

Published
2023
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