Daily-life gait quality as predictor of falls in older people - a 1-year prospect cohort study

K.S. van Schooten, M.A.G.M. Pijnappels, S.M. Rispens, P.J.M. Elders, P. Lips, A. Daffertshofer, P.J. Beek, J.H. van Dieen

Research output: Contribution to journalArticleAcademicpeer-review

4 Downloads (Pure)

Abstract

Falls can have devastating consequences for older people. We determined the relationship between the likelihood of fall incidents and daily-life behavior. We used wearable sensors to assess habitual physical activity and daily-life gait quality (in terms of e.g. stability, variability, smoothness and symmetry), and determined their predictive ability for time-to-first-andsecond falls. 319 older people wore a trunk accelerometer (Dynaport MoveMonitor, McRoberts) during one week. Participants further completed questionnaires and performed grip strength and trail making tests to identify risk factors for falls. Their prospective fall incidence was followed up for six to twelve months. We determined interrelations between commonly used gait characteristics to gain insight in their interpretation and determined their association
with time-to-falls. For all data -including questionnaires and tests- we determined the corresponding principal components and studied their predictive ability for falls. We showed that gait characteristics of walking speed, stride length, stride frequency, intensity, variability, smoothness, symmetry and complexity were often moderately to highly correlated (r > 0.4). We further showed that these characteristics were predictive of falls. Principal components dominated by history of falls, alcohol consumption, gait quality and muscle strength proved predictive for time-to-fall. The cross-validated prediction models had adequate to high accuracy (time dependent AUC of 0.66–0.72 for time-to-first-fall and 0.69–0.76 for-second-fall). Daily-life gait quality obtained from a single accelerometer on the trunk is predictive for falls. These findings confirm that ambulant measurements of daily behavior contribute substantially to the identification of elderly at (high) risk of falling.
Original languageEnglish
Article numbere0158623
Pages (from-to)1-13
JournalPLoS ONE
Volume11
Issue number7
DOIs
Publication statusPublished - 2016

Cite this

van Schooten, K. S., Pijnappels, M. A. G. M., Rispens, S. M., Elders, P. J. M., Lips, P., Daffertshofer, A., ... van Dieen, J. H. (2016). Daily-life gait quality as predictor of falls in older people - a 1-year prospect cohort study. PLoS ONE, 11(7), 1-13. [e0158623]. https://doi.org/10.1371/journal.pone.0158623
van Schooten, K.S. ; Pijnappels, M.A.G.M. ; Rispens, S.M. ; Elders, P.J.M. ; Lips, P. ; Daffertshofer, A. ; Beek, P.J. ; van Dieen, J.H. / Daily-life gait quality as predictor of falls in older people - a 1-year prospect cohort study. In: PLoS ONE. 2016 ; Vol. 11, No. 7. pp. 1-13.
@article{9f4c94c04d75449289730aa928474d35,
title = "Daily-life gait quality as predictor of falls in older people - a 1-year prospect cohort study",
abstract = "Falls can have devastating consequences for older people. We determined the relationship between the likelihood of fall incidents and daily-life behavior. We used wearable sensors to assess habitual physical activity and daily-life gait quality (in terms of e.g. stability, variability, smoothness and symmetry), and determined their predictive ability for time-to-first-andsecond falls. 319 older people wore a trunk accelerometer (Dynaport MoveMonitor, McRoberts) during one week. Participants further completed questionnaires and performed grip strength and trail making tests to identify risk factors for falls. Their prospective fall incidence was followed up for six to twelve months. We determined interrelations between commonly used gait characteristics to gain insight in their interpretation and determined their associationwith time-to-falls. For all data -including questionnaires and tests- we determined the corresponding principal components and studied their predictive ability for falls. We showed that gait characteristics of walking speed, stride length, stride frequency, intensity, variability, smoothness, symmetry and complexity were often moderately to highly correlated (r > 0.4). We further showed that these characteristics were predictive of falls. Principal components dominated by history of falls, alcohol consumption, gait quality and muscle strength proved predictive for time-to-fall. The cross-validated prediction models had adequate to high accuracy (time dependent AUC of 0.66–0.72 for time-to-first-fall and 0.69–0.76 for-second-fall). Daily-life gait quality obtained from a single accelerometer on the trunk is predictive for falls. These findings confirm that ambulant measurements of daily behavior contribute substantially to the identification of elderly at (high) risk of falling.",
author = "{van Schooten}, K.S. and M.A.G.M. Pijnappels and S.M. Rispens and P.J.M. Elders and P. Lips and A. Daffertshofer and P.J. Beek and {van Dieen}, J.H.",
year = "2016",
doi = "10.1371/journal.pone.0158623",
language = "English",
volume = "11",
pages = "1--13",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "7",

}

van Schooten, KS, Pijnappels, MAGM, Rispens, SM, Elders, PJM, Lips, P, Daffertshofer, A, Beek, PJ & van Dieen, JH 2016, 'Daily-life gait quality as predictor of falls in older people - a 1-year prospect cohort study' PLoS ONE, vol. 11, no. 7, e0158623, pp. 1-13. https://doi.org/10.1371/journal.pone.0158623

Daily-life gait quality as predictor of falls in older people - a 1-year prospect cohort study. / van Schooten, K.S.; Pijnappels, M.A.G.M.; Rispens, S.M.; Elders, P.J.M.; Lips, P.; Daffertshofer, A.; Beek, P.J.; van Dieen, J.H.

In: PLoS ONE, Vol. 11, No. 7, e0158623, 2016, p. 1-13.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Daily-life gait quality as predictor of falls in older people - a 1-year prospect cohort study

AU - van Schooten, K.S.

AU - Pijnappels, M.A.G.M.

AU - Rispens, S.M.

AU - Elders, P.J.M.

AU - Lips, P.

AU - Daffertshofer, A.

AU - Beek, P.J.

AU - van Dieen, J.H.

PY - 2016

Y1 - 2016

N2 - Falls can have devastating consequences for older people. We determined the relationship between the likelihood of fall incidents and daily-life behavior. We used wearable sensors to assess habitual physical activity and daily-life gait quality (in terms of e.g. stability, variability, smoothness and symmetry), and determined their predictive ability for time-to-first-andsecond falls. 319 older people wore a trunk accelerometer (Dynaport MoveMonitor, McRoberts) during one week. Participants further completed questionnaires and performed grip strength and trail making tests to identify risk factors for falls. Their prospective fall incidence was followed up for six to twelve months. We determined interrelations between commonly used gait characteristics to gain insight in their interpretation and determined their associationwith time-to-falls. For all data -including questionnaires and tests- we determined the corresponding principal components and studied their predictive ability for falls. We showed that gait characteristics of walking speed, stride length, stride frequency, intensity, variability, smoothness, symmetry and complexity were often moderately to highly correlated (r > 0.4). We further showed that these characteristics were predictive of falls. Principal components dominated by history of falls, alcohol consumption, gait quality and muscle strength proved predictive for time-to-fall. The cross-validated prediction models had adequate to high accuracy (time dependent AUC of 0.66–0.72 for time-to-first-fall and 0.69–0.76 for-second-fall). Daily-life gait quality obtained from a single accelerometer on the trunk is predictive for falls. These findings confirm that ambulant measurements of daily behavior contribute substantially to the identification of elderly at (high) risk of falling.

AB - Falls can have devastating consequences for older people. We determined the relationship between the likelihood of fall incidents and daily-life behavior. We used wearable sensors to assess habitual physical activity and daily-life gait quality (in terms of e.g. stability, variability, smoothness and symmetry), and determined their predictive ability for time-to-first-andsecond falls. 319 older people wore a trunk accelerometer (Dynaport MoveMonitor, McRoberts) during one week. Participants further completed questionnaires and performed grip strength and trail making tests to identify risk factors for falls. Their prospective fall incidence was followed up for six to twelve months. We determined interrelations between commonly used gait characteristics to gain insight in their interpretation and determined their associationwith time-to-falls. For all data -including questionnaires and tests- we determined the corresponding principal components and studied their predictive ability for falls. We showed that gait characteristics of walking speed, stride length, stride frequency, intensity, variability, smoothness, symmetry and complexity were often moderately to highly correlated (r > 0.4). We further showed that these characteristics were predictive of falls. Principal components dominated by history of falls, alcohol consumption, gait quality and muscle strength proved predictive for time-to-fall. The cross-validated prediction models had adequate to high accuracy (time dependent AUC of 0.66–0.72 for time-to-first-fall and 0.69–0.76 for-second-fall). Daily-life gait quality obtained from a single accelerometer on the trunk is predictive for falls. These findings confirm that ambulant measurements of daily behavior contribute substantially to the identification of elderly at (high) risk of falling.

U2 - 10.1371/journal.pone.0158623

DO - 10.1371/journal.pone.0158623

M3 - Article

VL - 11

SP - 1

EP - 13

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 7

M1 - e0158623

ER -