Test–retest reliability of trunk accelerometric gait analysis

Henriksen, Marius; Lund, Hans; Moe‐Nilssen, Rolf; Bliddal, Henning; Danneskiod-Samsøe, B.

doi:10.1016/s0966-6362(03)00069-9

Cited by 238 publications

(191 citation statements)

References 21 publications

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“…In contrast to the results of Hartmann et al and Henriksen et al, the temporal variables in the current study were least reliable [14][15]. A possible explanation for this could be use of a belt to attach the accelerometer.…”

Section: Figurecontrasting

confidence: 56%

“…excellent intra-and interrater and test-retest reliability of spatio-temporal gait parameters with ICCs between 0.86 and 0.99 and coefficients of variation (CVs) between 1 and 4 percent [14]. In addition, ICC values between 0.94 and 0.96 and measurement error values of 0.009 m for step length, 0.022 m for stride length, and 1.644 step/ min for cadence have been found [15]. Also, test-retest reliability for acceleration measures during gait has been proven to be high, with ICCs between 0.79 and 0.93 and CVs between 2.88 percent and 6.8 percent [15,20].…”

Section: Figurementioning

confidence: 87%

“…In addition, ICC values between 0.94 and 0.96 and measurement error values of 0.009 m for step length, 0.022 m for stride length, and 1.644 step/ min for cadence have been found [15]. Also, test-retest reliability for acceleration measures during gait has been proven to be high, with ICCs between 0.79 and 0.93 and CVs between 2.88 percent and 6.8 percent [15,20]. Meichtry et al reported high correlations (r > 0.82) for P values derived from trunk accelerometry and force plate measurements during gait and concluded the accelerometer is a valid measurement tool [18].…”

Section: Figurementioning

confidence: 97%

“…Several investigations have been conducted using accelerometers for the quantification of gait [13][14][15][16][17][18][19][20][21][22][23], chair rising [24][25], and balance [20,[25][26][27]. High reproducibility has been found using trunk accelerometry for the measurement of spatiotemporal gait parameters [14][15] and accelerations during gait [15,20].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reliability and validity of trunk accelerometry-derived performance measurements in a standardized heel-rise test in elderly subjects

Schmid

Hilfiker²,

Radlinger³

2011

JRRD

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Abstract-This cross-sectional study evaluated the intrasession reliability and concurrent validity of trunk accelerometry (with force plate measurements) for vertical ground reaction force and external mechanical power recorded during a standardized heel-rise (HR) test in 54 elderly subjects (mean +/-standard deviation age 81.2 +/-6.4 yr). Peak force as well as peak and average power revealed intraclass correlation coefficients of >0.75 and low standard errors of measurement for both the force plate-and the accelerometer-based curves. Correlation coefficients for these variables ranged from 0.95 to 0.98. The accelerometry-derived variables indicated significantly lower absolute values. Trunk accelerometry can be used as a reliable and valid tool for the quantification of the HR test in the elderly population. However, due to several limitations in the protocol, the use of this tool can currently only be recommended in a test-retest manner. Therefore, more research is needed to fully validate this tool for clinical use.

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Section: Figurecontrasting

confidence: 56%

Section: Figurementioning

confidence: 87%

Section: Figurementioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reliability and validity of trunk accelerometry-derived performance measurements in a standardized heel-rise test in elderly subjects

Schmid

Hilfiker²,

Radlinger³

2011

JRRD

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“…the step frequency (SF). Algorithms based on Autocorrelation (AC) 7,8 and Power Spectral Density (PSD) 9 have been used to this end. These methods require analysis of data collected over a time window (TW) of a given size.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Estimates of Step Frequency From a Wearable Gait Monitor

Punt¹,

Wittink²,

Bent³

et al. 2015

JournalMTM

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Background: Assessment of gait activity by accelerometry requires data analysis. Currently several methods are used to estimate step frequency. At present the relation between step frequency estimation, gait speed and minimal required time window length remains unknown.Aims: The purpose of the study was to assess the accuracy of estimates of step frequency (SF) from trunk acceleration data analyzed with commonly used algorithms and time window lengths, at a wide range of gait speeds.Method: Twenty healthy young subjects performed an incremental treadmill protocol from 1 km/h up to 6 km/h, with steps of 1 km/h. Each speed condition was maintained for two minutes. A waist worn accelerometer recorded trunk accelerations, while video analysis provided the correct number of steps taken during each gait speed condition. Accuracy of two commonly used signal analysis methods (autocorrelation, fast Fourier transformation) was examined with time windows of two, four and eight seconds.Results: Our main finding was that accuracy of SF estimates with fast Fourier transformation and autocorrelation improved with increasing time window size, only at the lower gait speeds. Accuracy of SF estimation was lower at low gait speeds independent of the algorithm and time window used. Conclusion:We recommend a minimum TW length of 4 seconds when using AC and PSD algorithms and when using the PSD algorithm to use spectral averaging, as this leads to better results at short TW and low gait speeds.

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