Validity and Reliability of the Track-UL Algorithm Compared With Kinovea Software for Measuring Upper-Limb Functional Range of Motion in People After Stroke: Cross-Sectional Observational Study.
Hatem Lazem, David Harris, Abi Hall and 7 others
PMID 42114045WHAT IT FOUND
A free video algorithm matched expert software closely for measuring arm movement in stroke survivors in the lab.
At home, only 13 of 27 could record usable video. Use it for clinic assessments, but expect technical failures with remote patient recording.
Key findings
01The Track-UL algorithm showed excellent agreement with Kinovea software for measuring shoulder and elbow range of motion in the laboratory setting.
02Only 13 of 27 participants were able to record home videos that were fit for analysis, limiting the tool's immediate utility for remote monitoring.
03The algorithm demonstrated good to excellent test-retest reliability for both shoulder and elbow joints on the affected side in the home setting.
STILL TO COME
How it was doneWhat they foundWhat it means for PTsWhat it means for OTs
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What it does not show
High dropout rate for home recordings: only 13 of 27 participants provided usable video, often due to technical difficulties or space constraints. The study excluded people with severe spasticity (Modified Ashworth Scale >2) and severe cognitive deficits, so results do not apply to these groups. 2D analysis is limited to a single plane and can be affected by compensatory movements like trunk rotation. The study was cross-sectional, so it validates concurrent accuracy but does not demonstrate the algorithm's ability to detect small changes in recovery over time.
Declared interests
Funded by the Egyptian government and the National Institute for Health and Care Research (NIHR) Biomedical Research Centre at the University of Exeter. One author is a Co-Founder and Director of DigiTherapix.
The easy way to misread this
Do not assume this tool is ready for home-based telemonitoring. Only 48.1% of participants could record usable video at home, and the authors recommend developing an app with real-time feedback to address these technical barriers before widespread remote use.
Summarised by AI from the full paper, without a clinician reviewing it. Check it against the source before it changes what you do. Read it on PubMed →