Jennifer Chan OTD, OTR/L
Assistant Professor of Clinical Occupational Therapy
Room: CHP 133
Jennifer Chan received her bachelor’s, master’s, and doctoral degrees at USC. During her doctoral residency at Keck Hospital of USC, she focused on advocating for the role of occupational therapy and working with patients with neurological impairments, from critical care to outpatient settings.
Dr. Chan currently serves as a clinical faculty member at Keck Hospital of USC and is committed to providing evidence-based, patient-centered care along the continuum of care. She is Neuro-IFRAH® Certified for the treatment and management of adults with hemiplegia from stroke or brain injury.
Doctorate of Occupational Therapy (OTD)
2019 | University of Southern California
Master of Arts (MA)
in Occupational Therapy
2018 | University of Southern California
Bachelor of Arts (BA)
in Cognitive Science
2015 | University of Southern California
Wathugala, M., Saldana, D., Juliano, J. M., Chan, J., & Liew, S.-L. (2019). Mindfulness meditation effects on poststroke spasticity: A feasibility study. Journal of Evidence-Based Integrative Medicine. Advance online publication. https://doi.org/10.1177/2515690X19855941 Show abstract
This study examined the feasibility of an adapted 2-week mindfulness meditation protocol for chronic stroke survivors. In addition, preliminary effects of this adapted intervention on spasticity and quality of life in individuals after stroke were explored. Ten chronic stroke survivors with spasticity listened to 2 weeks of short mindfulness meditation recordings, adapted from Jon Kabat-Zinn’s Mindfulness-Based Stress Reduction course, in a pre/post repeated measures design. Measures of spasticity, quality of life, mindfulness, and anxiety, along with qualitative data from participants’ daily journals, were assessed. On average, participants reported meditating 12.5 days of the full 15 days (mean 12.5 days, SD 0.94, range 8-15 days). Seven of the 10 participants wrote comments in their journals. In addition, there were no adverse effects due to the intervention. Exploratory preliminary analyses also showed statistically significant improvements in spasticity in both the elbow (P = .032) and wrist (P = .023) after 2 weeks of meditation, along with improvements in quality of life measures for Energy (P = .013), Personality (P = .026), and Work/Productivity (P = .032). This feasibility study suggests that individuals with spasticity following stroke are able to adhere to a 2-week home-based mindfulness meditation program. In addition, preliminary results also suggest that this adapted, short mindfulness meditation program might be a promising approach for individuals with spasticity following stroke. Future research should expand on these preliminary findings with a larger sample size and control group.
Keywords: stroke, mindfulness, spasticity, rehabilitation
Liew, S.-L., Anglin, J. M., Banks, N. W., Sondag, M., Ito, K. L., Kim, H., Chan, J., Ito, J., Jung, C., Khoshab, N., Lefebvre, S., Nakamura, W., Saldana, D., Schmiesing, A., Tran, C., Vo, D., Ard, T., Heydari, P., Kim, B., Aziz-Zadeh, L., Cramer, S. C., Liu, J., Soekadar, S., Nordvik, J.-E., Westlye, L. T., Wang, J., Winstein, C., Yu, C., Ai, L., Koo, B., Craddock, R. C., Milham, M., Lakich, M., Pienta, A., & Stroud, A. (2018). A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific Data, 5, 180011. https://doi.org/10.1038/sdata.2018.11 Show abstract
Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.