Effect of investigator observation on gait parameters in individuals with and without chronic low back pain

Vickers, J.G.; Reed, Austin; Decker, Robert C.; Conrad, Bryan P.; Olegario-Nebel, Marissa; Vincent, Heather K.

doi:10.1016/j.gaitpost.2017.01.002

Cited by 21 publications

(13 citation statements)

References 38 publications

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“…Second, as in other laboratory studies of gait and fall risk, women may alter their gait in laboratory conditions under investigator observation. 33 Third, the study has a small sample size with uneven numbers of women in the two risk groups. It did not attempt to classify fall risk in all women but only in women at upper and lower ends of risk.…”

Section: Discussionmentioning

confidence: 99%

Accelerometer-based predictive models of fall risk in older women: a pilot study

Hua

Quicksall

et al. 2018

npj Digital Med

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Current clinical methods of screening older adults for fall risk have difficulties. We analyzed data on 67 women (mean age = 77.5 years) who participated in the Objective Physical Activity and Cardiovascular Health (OPACH) study within the Women’s Health Initiative and in an accelerometer calibration substudy. Participants completed the short physical performance battery (SPPB), questions about falls in the past year, and a timed 400-m walk while wearing a hip triaxial accelerometer (30 Hz). Women with SPPB ≤ 9 and 1+reported falls ( n = 19) were grouped as high fall risk; women with SPPB = 10–12 and 0 reported falls ( n = 48) were grouped as low fall risk. Random Forests were trained to classify women into these groups, based upon traditional measures of gait and/or signal-based features extracted from accelerometer data. Eleven models investigated combined feature effects on classification accuracy, using 10-fold cross-validation. The models had an average 73.7% accuracy, 81.1% precision, and 0.706 AUC. The best performing model including triaxial data, cross-correlations, and traditional measures of gait had 78.9% accuracy, 84.4% precision, and 0.846 AUC. Mediolateral signal-based measures—coefficient of variance, cross-correlation with anteroposterior accelerations, and mean acceleration—ranked as the top 3 features. The classification accuracy is promising, given research on probabilistic models of falls indicates accuracies ≥80% are challenging to achieve. The results suggest accelerometer-based measures captured during walking are potentially useful in screening older women for fall risk. We are applying algorithms developed in this paper on an OPACH dataset of 5000 women with a 1-year prospective falls log and week-long, free-living accelerometer data.

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Section: Discussionmentioning

confidence: 99%

Accelerometer-based predictive models of fall risk in older women: a pilot study

Hua

Quicksall

et al. 2018

npj Digital Med

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“…Wearable technology allows researchers to record patients' physical activities in unobserved, daily-living environments over extended periods of time. This data can reflect the real gait performance of the patients since the controlled laboratory environment, while being observed, may change the performance of patients [23]. The successful employment of machine learning methods in gait analysis makes it possible to extract the most informative gait outcomes from the accelerometer sensor data [20].…”

Section: Introductionmentioning

confidence: 99%

Effect of Level of Sensitization on Gait in Chronic Low Back Pain: Insights from a Machine Learning Approach

Zheng

Reneman

et al. 2021

Preprint

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Background: Despite central sensitization (CS) often presents in patients with chronic low back pain (CLBP), there is a lack of quantitative and qualitative analysis of the effect of CS on gait performance. The study aimed to investigate the daily-living gait performance of patients with CLBP with low and high CS levels (CLBP- and CLBP+, respectively). Method：Forty-two patients with CLBP were included. CS was assessed by Central Sensitization Inventory (CSI). Patients were classified according to low or high CSI score (23 CLBP- and 19 CLBP+). Patients wore a 3D accelerometer for about one week. From each patient, 4 days of accelerometer-data were randomly selected. For each day data, continuous gait cycles were extracted using a Fast Fourier Transform-based and a zero-cross method. For all gait cycles in one day, 36 gait outcomes representing variables related to pace, regularity, smoothness, local stability and predictability of gait were calculated. A Random Forest classifier was trained to classify CLBP- and CLBP+ groups based on gait outcomes and SHapley Additive exPlanations (SHAP) method was used to explain the differences between groups in gait outcomes. Results：The Random Forest classifier could accurately recognize the CLBP- and CLBP+ groups (accuracy = 84.4%, F1-score = 82.6%). SHAP reported that the most differences between CLBP- and CLBP+ groups were: index of harmonicity-vertical and harmonic ratio-mediolateral (gait smoothness), stride frequency variability- mediolateral/anteroposterior, stride length variability (gait variability), stride regularity-mediolateral (gait regularity), maximal Lyapunov exponent-vertical/mediolateral and maximal Lyapunov exponent per stride-vertical (gait stability), and sample entropy-anteroposterior (gait predictability). Conclusions： CLBP- and CLBP+ presented different motor control strategies. CLBP- presented a more “loose” control, including higher gait smoothness and stability. CLBP+ presented a more “tight” control, including a more regular, less variable and more predictable gait pattern.

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“…Gait research is regularly completed in a laboratory setting, yet most of a person’s daily walking occurs outside of the laboratory, during activities of daily living. Currently, much of the in-laboratory findings are interpreted and translated to daily walking in a real-world setting, although this comparison assumes that gait in the laboratory setting is consistent with normal day-to-day gait 2 – 4 .…”

Section: Introductionmentioning

confidence: 99%

“…Prior studies have acknowledged the effects of observation on gait in older, unhealthy 4 , 9 , 13 , and injured populations 2 , 14 . Similarly, awareness of data collection affected gait kinematics during individual versus continuous trials, suggesting study conditions also affect the measured outcomes during analysis 15 .…”

Section: Introductionmentioning

confidence: 99%

All eyes on you: how researcher presence changes the way you walk

Friesen

Zhang

Monaghan

et al. 2020

Sci Rep

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Most human movement research takes place within controlled laboratories where researchers observe participant movement. Because a majority of daily activity is performed without observation, we hypothesized movement within a laboratory would vary when there was a small, large or absence of research group. We also hypothesized that personality type would influence movement during observation. Participants completed a personality questionnaire, then walked in a laboratory during three different conditions: no research group (no researchers), small research group (2 researchers), and large research group (6–10 researchers). Results revealed spatiotemporal parameters were altered between conditions, however personality type did not influence changes in movement. As the number of researchers increased, gait speed, cadence, and stride length increased, and step duration decreased. Gait speed increased by .03 m/s from the no research group to the small research group, by .06 m/s from the no research group to the large research group, and by .03 m/s from the small to large research group (all p values < .001). Understanding how researcher observation modifies movement is important and affects the replicability of results, as well as the interpretation of laboratory-based movement studies to activities of daily living in real world settings.

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Effect of investigator observation on gait parameters in individuals with and without chronic low back pain

Cited by 21 publications

References 38 publications

Accelerometer-based predictive models of fall risk in older women: a pilot study

Accelerometer-based predictive models of fall risk in older women: a pilot study

Effect of Level of Sensitization on Gait in Chronic Low Back Pain: Insights from a Machine Learning Approach

All eyes on you: how researcher presence changes the way you walk

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