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.
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.
PURPOSE: Chronic physical activity is associated with reduced depression and improved mood. Even acute bouts of exercise have been shown to improve many mood parameters. However, these findings are investigated under controlled laboratory conditions, not the context where most individuals engage in physical activity. Thus, the current study sought to determine whether a group exercise class would lead to acute improvements in mood parameters. METHODS: Subjects recruited from DailyBurn365 class members (n=16, 81% female) consented to participate in the study in compliance with the New York University IRB. Participants completed mood questionnaires, consisting of a modified profile of mood states and state trait anxiety inventory, 10-30 minutes before the exercise class. Polar H7 heart rate (HR) monitors were used during the group exercise class to record average exercise HR over the 30-45minute exercise session. After the exercise, all participants were given a 10-minute recovery period. Following recovery, the participants repeated the mood questionnaires. The participants repeated this process on 2 separate days, at least 1 week apart, allowing us to compare the effects of exercise and the effects of repeated testing using a quasi-experimental design. RESULTS: Sixteen participants successfully completed 2 separate sessions of exercise with both pre and post exercise measurements. There were no differences in the percent HR reserve of exercise for the participants between the two sessions (t=-0.25, p=0.81). In addition, there were no differences reported in pre-exercise mood parameters: depression, hostility, vigor, or stress for day 1 versus day 2 (t=-0.99, p=0.34; t=-1.22, p=0.24; t=-0.15, p=0.88; and t=-0.56, p=0.58 respectively). Following exercise on both days, there were significant improvements in all mood parameters: depression (day1: t=3.
Gait alterations due to fatigue have been evaluated in strenuous jobs like firefighting but less is known about gait changes after less strenuous prolonged activity. PURPOSE: We tested the hypothesis that gait changes would be greater after manual labor tasks performed in the heat (30°C, 50% RH) vs temperate (20°C, 30% RH). METHODS: Fifteen healthy (BMI 23.6 ± 3.0) subjects (9 females) completed two experimental visits of simulated manual labor tasks in an environmental chamber set to either the hot (HT) or temperate (CON) condition. Tasks consisted of 6 rounds of a circuit of brick-laying for ten-minutes, shoveling rubber mulch for five-minutes, and five-minutes of rest for a total of two hours. Gait characteristics were evaluated before and after exertion by the subject walking at a self-selected pace over an instrumented walkway. Walking speed, stride length, right leg single support, swing time, stance time, gait cycle time, double support time and double support percentage were analyzed. Values are reported as mean ± SD. RESULTS: A time x condition interaction was seen for stride length (cm), double support time (sec) and double support percentage. No main effect or interactions were found for walking speed (steps/min) or right single support (sec). Swing time (pre: 0.41±0.03 vs post: 0.40±0.03 sec; p=0.03), stance time (pre: 0.73±0.08 vs post: 0.71±0.07 sec; p=0.02) and gait cycle time (pre: 1.14 ± 0.10 vs post: 1.11 ± 0.09 sec; p=0.01) decreased in the CO trial from pre to post. Heart rate increased between the seated baseline and the end of the sixth round in the HT condition (69±6 vs 137±2; p<0.001) and the CON condition (66±7 vs 127±24; p=0.02) and was greater in the HT condition. Core temperature (Tc) increased in the CON (37.3±0.2 vs 37.8±0.28°C; p<0.001) and HT (37.2±0.3 vs 37.8±0.4°C; p<0.001). No difference in Tc (p=0.53) was found between conditions. CONCLUSIONS: Minor changes in gait were identified after manual labor tasks in CON conditions which were not seen in the HT condition. Greater sympathetic arousal from work in the heat could be masking the changes seen after identical work in cool conditions.
In the United States, approximately 18% of adults are affected by anxiety. Nearly $42 billion are spent annually in the U.S. toward alleviating this mood disorder. However, only 10% of those with anxiety are receiving appropriate and effective treatment due to lack of identification. The purpose of this study was to identify gait patterns that are correlated with anxiety to improve probability of detection of this mood disorder. Past studies have examined gait and anxiety in Parkinson’s disease or in healthy individuals with induced anxiety. Our study tries to determine identifiable gait patterns that could signify the presence of increased anxiety in those without a diagnosed mood disorder. Participants (N=133, Males=50, Females=83, Age=25.8±7.96, BMI=24.8±3.8) ages 18 to 69 completed a Profile of Mood Survey (POMS) to assess current feelings of anxiety and completed a two‐minute walk around a 6 m track. Gait data was collected utilizing APDM mobility monitors. A backwards linear regression model was utilized to identify gait correlates of anxiety. The model predicted 13.1% of variance (R2=0.215, F= 2.553, p<.009) associated with anxiety. Our results showed that increased feelings of anxiety were associated with an increase in lateral anticipatory postural adjustment, lateral neck movement, toe‐out angle, transverse range of motion in the back, turn angles, and number of steps in a turn. Anxiety was also associated with decreased neck rotation, asymmetries in mid‐swing elevation, step variability, and sagittal range of motion in the back. Our results indicate that as feelings of anxiety increase in intensity, subjects present with a more narrow base of support, which may cause the increase in lateral movements during gait. Identifying gait patterns associated with anxiety mood disorders may lead to improved identification of individuals that may benefit from preventative and/or early intervention care plans. Future research should focus on whether increased anxiety is associated with a greater input from higher brain areas that overpower the rhythmic neural output of learned central pattern generators of gait. Additionally, caution should be used to ensure that multiple diagnoses are considered in the presentation of distinct characteristics of gait patterns.
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