Movement Intensity in the Minutes Before a Fall: A Lag-Controlled, Activity-Matched Time-Stratified Case-Crossover Analysis of Home Monitoring in Multiple Sclerosis
4 affiliations
- 1Department of Mathematics, University of Architecture, Civil Engineering and Geodesy (UACEG), 1 Hristo Smirnenski Blvd., 1164 Sofia, Bulgaria
- 2Department of Applied Computer Science and Mathematical Modelling, Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Słoneczna 54, 10-710 Olsztyn, Poland
- 3Department of Mathematical Modelling and Numerical Methods, Faculty of Applied Mathematics and Informatics, Technical University of Sofia, 8 “Kliment Ohridski” Blvd., 1000 Sofia, Bulgaria
- 4Faculty of Mathematics and Informatics, Sofia University “St. Kliment Ohridski”, 5 James Bourchier Blvd., 1164 Sofia, Bulgaria
Abstract
Background: Fall-risk research in multiple sclerosis usually asks who falls and does not address what changes in the minutes before a fall. Methods: We analyzed continuous home inertial monitoring of 26 people with multiple sclerosis using a time-stratified case-crossover design. Each fall was compared with referent windows from the same participant on other days at the same clock time. Results: After one participant whose accelerometer was not on a 1 g scale was excluded, 48 falls and 285 referents remained in 12 informative strata. Per standard deviation, gravity-removed dynamic acceleration had an odds ratio of 3.05 (95% CI 1.66–5.63). The estimate persisted when the window ended 10, 30, or 60 s before the annotated fall, although these designs overlap the primary one and are not independent tests. Referents from pre-fall days only gave 3.46 (1.67–7.16). When activity was held comparable, the estimate fell to 2.11 (1.17–3.79) within ambulatory windows and to 1.38 (0.86–2.22, p = 0.183) against referents from walking periods. Conclusions: These results suggest that much of the association reflects the fact that people fall while they are moving. No released fall lies in unrecorded time, which indicates that the public dataset retains sensor-captured falls.
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BibTeX
@article{naskinova2026movement,
title = {{Movement Intensity in the Minutes Before a Fall: A Lag-Controlled, Activity-Matched Time-Stratified Case-Crossover Analysis of Home Monitoring in Multiple Sclerosis}},
author = {Irina Naskinova and Mikhail Kolev and Meglena Lazarova and Hristo Kalinov and Velin Klisurov},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15218323},
url = {https://www.mdpi.com/2077-0383/15/21/8323},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Movement Intensity in the Minutes Before a Fall: A Lag-Controlled, Activity-Matched Time-Stratified Case-Crossover Analysis of Home Monitoring in Multiple Sclerosis
AU - Irina Naskinova
AU - Mikhail Kolev
AU - Meglena Lazarova
AU - Hristo Kalinov
AU - Velin Klisurov
PY - 2026
DA - 2026/10/26
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15218323
UR - https://www.mdpi.com/2077-0383/15/21/8323
AB - Background: Fall-risk research in multiple sclerosis usually asks who falls and does not address what changes in the minutes before a fall. Methods: We analyzed continuous home inertial monitoring of 26 people with multiple sclerosis using a time-stratified case-crossover design. Each fall was compared with referent windows from the same participant on other days at the same clock time. Results: After one participant whose accelerometer was not on a 1 g scale was excluded, 48 falls and 285 referents remained in 12 informative strata. Per standard deviation, gravity-removed dynamic acceleration had an odds ratio of 3.05 (95% CI 1.66–5.63). The estimate persisted when the window ended 10, 30, or 60 s before the annotated fall, although these designs overlap the primary one and are not independent tests. Referents from pre-fall days only gave 3.46 (1.67–7.16). When activity was held comparable, the estimate fell to 2.11 (1.17–3.79) within ambulatory windows and to 1.38 (0.86–2.22, p = 0.183) against referents from walking periods. Conclusions: These results suggest that much of the association reflects the fact that people fall while they are moving. No released fall lies in unrecorded time, which indicates that the public dataset retains sensor-captured falls.
SN - 2077-0383
ER -