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#apaperaday: Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases

In today’s #apaperaday, Prof. Aartsma-Rus reads and comments on the paper titled: Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases

Today’s pick is from Fronters In Pharmacology by Ferrer-Mallol et al on computer analysis of functional tasks performed by Duchenne patients at home. Collaboration between Aparito Health and Duchenne UK doi 10.3389/fphar.2022.916714.

Duchenne is a progressive disease characterized by loss of function. Loss of ambulation is a milestone but authors stress loss of being able to transfer (eg from bed to wheelchair) has an impact as well as this implies the need for additional aids and increased caregiver burden.

Most outcome measures used in clinical trials either study ambulatory function or arm function, but not this transfer stage in between. Furthermore these outcomes are assessed in a hospital ad hoc and not at home longitudinally.

Authors suggest tasks to be done at home and captured by caregivers. The videos are then uploaded and analyzed through computer analysis. All of this (task instructions, video instructions and uploading) was part of the DMD Home app built for this purpose.

11 patients were included in a pilot, 6 ambulant, 5 non ambulant. Sadly 0 in the transfer stage. 8 patients and families actively participated and uploaded 62 videos of which 52 were useful.

Tasks included moving hands to the head while sitting or standing (ambulatory patients only), rising from seat or walking. During analysis parameters like time, smoothness of movement, compensatory movements, symmetry of movement etc could be measured by analysis tool.

Authors also assessed how user friendly the app was (very). The only suggestion was to improve timing of reminders for performing tasks. Tasks were easy to perform based on instructions. However authors indicate that patients and families interpreted the tasks in different ways.

This different interpretation complicated analysis. Authors conclude that loss of symmetry, smooth movement and increases in compensatory movements are signs of disease progression that they can detect with the tool.

Of course this is a pilot and more work is needed, eg to make instructions more clear (I propose a training session with the families), to standardize equipment (eg low vs high backed chair, or height of chairs).

Natural history has to be captured, hopefully also with patients in transfer transition stage. But the study shows feasibility which is an important first step. Also, with the pandemic it is clear that also including analyses at home is prudent.

And I like that it was a collaboration with patients, so things that mattered to them were captured. Looking forward to additional work from this group and others in the same field.