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Literature suggests that anxiety affects gait and balance among young adults. However, previous studies using machine learning (ML) have only used gait to identify individuals who report feeling anxious. Therefore, the purpose of this study was to identify individuals who report feeling anxious at that time using a combination of gait and quiet balance ML. Using a cross-sectional design, participants (n = 88) completed the Profile of Mood Survey-Short Form (POMS-SF) to measure current feelings of anxiety and were then asked to complete a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a two-minute walk around a 6 m track while wearing nine APDM mobility sensors. Results from our study finds that Random Forest classifiers had the highest median accuracy rate (75%) and the five top features for identifying anxious individuals were all gait parameters (turn angles, variance in neck, lumbar rotation, lumbar movement in the sagittal plane, and arm movement). Post-hoc analyses suggest that individuals who reported feeling anxious also walked using gait patterns most similar to older individuals who are fearful of falling. Additionally, we find that individuals who are anxious also had less postural stability when they had visual input; however, these individuals had less movement during postural sway when visual input was removed.

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Failure to obtain the recommended 7–9 h of sleep has been associated with injuries in youth and adults. However, most research on the influence of prior night’s sleep and gait has been conducted on older adults and clinical populations. Therefore, the objective of this study was to identify individuals who experience partial sleep deprivation and/or sleep extension the prior night using single task gait. Participants (n = 123, age 24.3 ± 4.0 years; 65% female) agreed to participate in this study. Self-reported sleep duration of the night prior to testing was collected. Gait data was collected with inertial sensors during a 2 min walk test. Group differences (<7 h and >9 h, poor sleepers; 7–9 h, good sleepers) in gait characteristics were assessed using machine learning and a post-hoc ANCOVA. Results indicated a correlation (r = 0.79) between gait parameters and prior night’s sleep. The most accurate machine learning model was a Random Forest Classifier using the top 9 features, which had a mean accuracy of 65.03%. Our findings suggest that good sleepers had more asymmetrical gait patterns and were better at maintaining gait speed than poor sleepers. Further research with larger subject sizes is needed to develop more accurate machine learning models to identify prior night’s sleep using single-task gait.

Background Fatigue has been postulated to influence postural control, which may lead to an increased risk for falling among older adults.1 Mental and physical energy and fatigue have recently been reported to be four distinct mood states.2,3 Thus, to test Grobe’s1 theory of the influence of fatigue on postural control, we must examine all four mood states. Objective This study examined the influence of mental and physical energy and fatigue on postural control. Methods Adults (N=133, Males=50, Females=83, Age=25.8±7.96, BMI=24.8±3.8) aged 18-69 years were recruited from the community and asked to complete a series of surveys that measured their current mental and physical energy and fatigue states. After the completion of the surveys, subjects were instructed to complete the modified Clinical Test of Sensory Interaction in Balance (mCTSIB) using the APDM mobility monitors. Necessary assumptions were verified, and four multivariate multiple regression models were developed. Results Analyses yielded a significant association between posture and state mental energy (p=.048), but only when subjects were standing with their eyes closed while on a foam surface. Increased feelings of mental energy were associated with decreased total frequency dispersion (b=-358.62) and increased jerk in the coronal plane (b=11.78). No other associations were found. Discussion Results of our study suggest that as mental energy decreases there is a concomitant decrease in postural control when subjects are placed in conditions where they are unable to rely on visual feedback on unstable surfaces to maintain balance. Progressive increases in postural instability lead to increased risks of falls, most commonly in the elderly population. Falls are a significant risk factor for mortality. This study supports the clinical recommendation either to: (1) improve integration of vestibular and somatosensory input into postural control; or (2) train compensatory strategies for low lighting environments during episodes of decreased mental energy.