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PURPOSE: Chemotherapy-induced peripheral neuropathy (CIPN) and falls can be persistent side effects of cancer treatment. Standing postural sway and gait tests with body-worn, inertial sensors provide objective digital balance and gait measures that represent several different domains controlling mobility. Specific domains of balance and gait that related to neuropathy and falls are unknown. The aim of this study was to determine which domains of balance and gait differed between cancer survivors who report (1) CIPN symptoms versus no symptoms, (2) a history of falls in the past 6 months versus no falls, and (3) prospective falls over 12 months versus no falls. METHODS: Postural sway during 30 seconds of quiet standing and gait characteristics from a 7-m timed up and go test were recorded with six synchronized inertial sensors (Opals by APDM Wearable Technologies, a Clario Company) in 425 older, female cancer survivors (age: 62 ± 6 years). A principal component analysis (PCA) approach was used to identify independent domains of mobility from 15 balance and gait measures. RESULTS: PCA analysis revealed five independent domains (PC1 = sway amplitude, PC2 = gait pace, PC3 = sway frequency, PC4 = gait spatial-temporal, and PC5 = turning) that accounted for 81% of the variance of performance. Cancer survivors who reported CIPN symptoms had significantly higher sway frequency (PC3) than asymptomatic survivors. Past fallers had significantly larger sway area (PC1) and slower gait pace (PC2) than nonfallers. Prospective fallers showed a significantly smaller stride length (PC4) than nonfallers. CONCLUSION: Digital balance and gait measures using wearable sensors during brief standing and walking tests provide objective metrics of CIPN-related mobility impairment and fall risk that could be useful for oncology clinical trials. [ABSTRACT FROM AUTHOR]

Background: Progressive loss of standing balance is a feature of Friedreich's ataxia (FRDA). Objectives: This study aimed to identify standing balance conditions and digital postural sway measures that best discriminate between FRDA and healthy controls (HC). We assessed test–retest reliability and correlations between sway measures and clinical scores. Methods: Twenty‐eight subjects with FRDA and 20 HC completed six standing conditions: feet apart, feet together, and feet tandem, both with eyes opened (EO) and eyes closed. Sway was measured using a wearable sensor on the lumbar spine for 30 seconds. Test completion rate, test–retest reliability with intraclass correlation coefficients, and areas under the receiver operating characteristic curves (AUCs) for each measure were compared to identify distinguishable FRDA sway characteristics from HC. Pearson correlations were used to evaluate the relationships between discriminative measures and clinical scores. Results: Three of the six standing conditions had completion rates over 70%. Of these three conditions, natural stance and feet together with EO showed the greatest completion rates. All six of the sway measures' mean values were significantly different between FRDA and HC. Four of these six measures discriminated between groups with >0.9 AUC in all three conditions. The Friedreich Ataxia Rating Scale Upright Stability and Total scores correlated with sway measures with P‐values <0.05 and r‐values (0.63–0.86) and (0.65–0.81), respectively. Conclusion: Digital postural sway measures using wearable sensors are discriminative and reliable for assessing standing balance in individuals with FRDA. Natural stance and feet together stance with EO conditions suggest use in clinical trials for FRDA. © 2024 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Background: It is unknown whether medication status (off and on levodopa) or laboratory versus home settings plays a role in discriminating fallers and non‐fallers in people with Parkinson's disease (PD). Objectives: To investigate which specific digital gait and turning measures, obtained with body‐worn sensors, best discriminated fallers from non‐fallers with PD in the clinic and during daily life. Methods: We recruited 34 subjects with PD (17 fallers and 17 non‐fallers based on the past 6 month's falls). Subjects wore three inertial sensors attached to both feet and the lumbar region in the laboratory for a 3‐minute walking task (both off and on levodopa) and during daily life activities for a week. We derived 24 digital (18 gait and 6 turn) measures from the 3‐minute walk and from daily life. Results: In clinic, none of the gait and turning measures collected during on levodopa state were significantly different between fallers and non‐fallers. In contrast, digital measures collected in the off levodopa state were significantly different between groups, (average turn velocity, average number of steps to complete a turn, and variability of gait speed, P < 0.03). During daily life, the variability of average turn velocity (P = 0.023) was significantly different in fallers than non‐fallers. Last, the average number of steps to complete a turn was significantly correlated with the patient‐reported outcomes. Conclusions: Digital measures of turning, but not gait, were different in fallers compared to non‐fallers with PD, in the laboratory when off medication and during a daily life. [ABSTRACT FROM AUTHOR]

Freezing-of-Gait (FoG) is a syndrome of Parkinson's disease defined by episodes when patients show a complete inability to take a step or continue with their locomotion. In order to develop closed-loop therapeutic strategies, including deep brain stimulation, a reliable means of detecting freezing episodes is required. By using wearable accelerometers, freezing episodes can be detected with energy-based algorithms when the ratio of the energy in the freeze band (3 to 8 Hz) to that of the locomotion band (0.5 to 3 Hz) is above a patient-specific threshold. However, due to the great variability in patient activity type, walking style, and freezing pattern, this detection method often does not work. Here we describe a new FoG-detection method that captures temporal, spatial, and physiological features and uses a support-vector-machine to classify freezing episodes. Since our method uses more diverse features, it is able to more robustly detect FoG events. We have shown that when the energy-based method fails (e.g., area under the receiver operator curve is ~0.5), our new method performs well (e.g., area under ROC curve is 0.96).