TY - GEN
T1 - 'Are They Doing Better In The Clinic Or At Home?'
T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
AU - Seals, Ayanna
AU - Pilloni, Giuseppina
AU - Kim, Jin
AU - Sanchez, Raul
AU - Rizzo, John-Ross
AU - Charvet, Leigh
AU - Nov, Oded
AU - Dove, Graham
N1 - Funding Information:
We would like to thank all of the clinicians that participated in this study. We gratefully acknowledge support from the National Science Foundation (Awards 2129076; 1928614).
Publisher Copyright:
© 2022 ACM.
PY - 2022/4/29
Y1 - 2022/4/29
N2 - Walking impairment is a debilitating symptom of Multiple Sclerosis (MS), a disease affecting 2.8 million people worldwide. While clinicians' in-person observational gait assessments are important, research suggests that data from wearable sensors can indicate early onset of gait impairment, track patients' responses to treatment, and support remote and longitudinal assessment. We present an inquiry into supporting the transition from research to clinical practice. Co-design by HCI, biomedical, neurology and rehabilitation researchers resulted in a data-rich interface prototype for augmented gait analysis based on visualized sensor data. We used this as a prompt in interviews with ten experienced clinicians from a range of MS rehabilitation roles. We find that clinicians value quantitative sensor data within a whole patient narrative, to help track specific rehabilitation goals, but identify a tension between grasping critical information quickly and more detailed understanding. Based on the findings we make design recommendations for data-rich remote rehabilitation interfaces.
AB - Walking impairment is a debilitating symptom of Multiple Sclerosis (MS), a disease affecting 2.8 million people worldwide. While clinicians' in-person observational gait assessments are important, research suggests that data from wearable sensors can indicate early onset of gait impairment, track patients' responses to treatment, and support remote and longitudinal assessment. We present an inquiry into supporting the transition from research to clinical practice. Co-design by HCI, biomedical, neurology and rehabilitation researchers resulted in a data-rich interface prototype for augmented gait analysis based on visualized sensor data. We used this as a prompt in interviews with ten experienced clinicians from a range of MS rehabilitation roles. We find that clinicians value quantitative sensor data within a whole patient narrative, to help track specific rehabilitation goals, but identify a tension between grasping critical information quickly and more detailed understanding. Based on the findings we make design recommendations for data-rich remote rehabilitation interfaces.
KW - data visualization
KW - gait assessments
KW - multiple sclerosis
KW - remote care
KW - transition to clinical practice
KW - wearables
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UR - http://www.scopus.com/inward/citedby.url?scp=85130585746&partnerID=8YFLogxK
U2 - 10.1145/3491102.3501989
DO - 10.1145/3491102.3501989
M3 - Conference contribution
AN - SCOPUS:85130585746
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 30 April 2022 through 5 May 2022
ER -