Highlights
- •People with MS fall frequently.
- •The gold standard for fall reporting is prospective self-report fall calendars.
- •There is increasing interest in automated fall detection.
- •We compare here three methods of fall counting, used concurrently.
Abstract
Background Falls occur across the population but are more common, and have more negative
sequelae, in people with multiple sclerosis (MS). Given the prevalence and impact
of falls, accurate measures of fall frequency are needed. This study compares the
sensitivity and false discovery rates of three methods of fall detection: the current
gold standard, prospective paper fall calendars, real-time self-reporting and automated
detection, the latter two from a novel body-worn device.
Methods Falls in twenty-five people with MS were recorded for eight weeks with prospective
fall calendars, real-time body-worn self-report, and an automated body-worn detector
concurrently. Eligible individuals were adults with MS enrolled in a randomized controlled
trial of a fall prevention intervention. Entry criteria were at least two falls or
near-falls in the previous two months, Expanded Disability Status Scale ≤ 6.0, community
dwelling, and no MS relapse in the previous month. The sensitivity (proportion of
true falls detected) and false discovery rates (proportion of false reports generated)
of the fall detection methods were compared. A true fall was a fall reported by at
least two methods. A false report was a fall reported by only one method. The trial
is registered on ClinicalTrials.gov (NCT02583386) and is closed.
Results In the 1,276 person-days of fall counting with all three methods in use simultaneously
there were 1344 unique fall events. Of these, 8.5% (114) were true falls and 91.5%
(1230) were false reports. Fall calendars had the lowest sensitivity (0.614) and the
lowest false discovery rate (0.067). The automated detector had the highest sensitivity
(0.921) and the highest false discovery rate (0.919). All methods generated under
one false report per day. There were no fall detection-related adverse events.
Conclusion Fall calendars likely underestimate fall frequency by around 40%. The automated
detector evaluated here misses very few falls but likely overestimates the number
of falls by around one fall per day. Additional research is needed to produce an ideal
fall detection and counting method for use in clinical and research applications.
Funding United States Department of Veterans Affairs, Rehabilitations Research and
Development Service
Keywords
Abbreviations:
ADL (activities of daily living), EDSS (expanded disability status scale), FFF (free from falls), GPS (global positioning system), IMU (inertial monitoring unit), IQR (interquartile range), MS (multiple sclerosis), OHSU (oregon health & science university), PwMS (people with MS), sd (standard deviation), ToF (time-of-flight), VAPORHCS (veterans affairs portland health care system)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: September 18, 2021
Accepted:
September 16,
2021
Received in revised form:
September 6,
2021
Received:
May 21,
2021
Identification
Copyright
© 2021 Elsevier B.V. All rights reserved.
