Ayanna Seals
New York University, New York, New York, United States
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.
https://dl.acm.org/doi/abs/10.1145/3491102.3501989
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)