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Objective measures of balance and gait have the potential to improve prediction of future fallers because balance and gait impairments are common precursors. We used the Instrumented Stand and Walk Test (ISAW) with wearable, inertial sensors to maximize the domains of balance and gait evaluated in a short test. We hypothesized that ISAW objective measures across a variety of gait and balance domains would improve fall prediction beyond history of falls and better than gait speed or dual-task cost on gait-speed. We recruited 214 high-functioning older men (mean 82 years), of whom 91 participants (42.5%) had one or more falls in the 12 months following the ISAW test. The ISAW test involved 30 s of stance followed by a 7-m walk, turn, and return. We examined regression models for falling using 17 ISAW metrics, with and without age and fall history, and characterize top-performing models by AUC and metrics included. The ISAW test improved distinguishing between future fallers and non-fallers compared to age and history of falls, alone (AUC improved from 0.69 to 0.75). Models with 1 ISAW metric usually included a postural sway measure, models with 2 ISAW measures included a turning measure, models with 3 ISAW measures included a gait variability measure, and models with 4 or 5 measures added a gait initiation measure. Gait speed and dual-task cost did not distinguish between fallers and non-fallers in this high-functioning cohort. The best fall-prediction models support the notion that older people may fall due to a variety of balance and gait impairments.

There is a lack of recommendations for selecting the most appropriate gait measures of Parkinson’s disease (PD)-specific dual-task costs to use in clinical practice and research. We aimed to identify measures of dual-task costs of gait and turning that best discriminate performance in people with PD from healthy individuals. We also investigated the relationship between the most discriminative measures of dual-task costs of gait and turning with disease severity and disease duration. People with mild-to-moderate PD (n = 144) and age-matched healthy individuals (n = 79) wore 8 inertial sensors while walking under single and dual-task (reciting every other letter of the alphabet) conditions. Outcome measures included 26 objective measures within four gait domains (upper/lower body, turning and variability). The area under the curve (AUC) from the receiver-operator characteristic plot was calculated to compare discriminative ability of dual-task costs on gait across outcome measures. PD-specific, dual-task interference was identified for arm range of motion, foot strike angle, turn velocity and turn duration. Arm range of motion (AUC = 0.73) and foot strike angle (AUC = 0.68) had the largest AUCs across dual-task costs measures and they were associated with disease severity and/or disease duration. In contrast, the most commonly used dual-task gait measure, gait speed, showed an AUC of only 0.54. Findings suggest that people with PD rely more than healthy individuals on executive-attentional resources to control arm swing, foot strike, and turning, but not gait speed. The dual-task costs of arm range of motion best discriminated people with PD from healthy individuals.

Identifying digital biomarkers of mobility is important for clinical trials in Parkinson’s disease (PD). To determine which digital outcome measures of mobility discriminate mobility in people with PD from healthy control (HC) subjects over a week of continuous monitoring. We recruited 29 people with PD, and 27 age-matched HC subjects. Subjects were asked to wear three inertial sensors (Opal by APDM) attached to both feet and to the lumbar region, and a subset of subjects also wore two wrist sensors, for a week of continuous monitoring. We derived 43 digital outcome measures of mobility grouped into five domains. An Area Under Curve (AUC) was calculated for each digital outcome measures of mobility, and logistic regression employing a ‘best subsets selection strategy’ was used to find combinations of measures that discriminated mobility in PD from HC. Duration of recordings was 66±14 hours in the PD and 59±16 hours in the HC. Out of a total of 43 digital outcome measures of mobility, we found six digital outcome measures of mobility with AUC > 0.80. Turn angle (AUC = 0.89, 95% CI: 0.79–0.97) and swing time variability (AUC = 0.87, 95% CI: 0.75–0.96) were the most discriminative individual measures. Turning measures were most consistently selected via the best subsets strategy to discriminate people with PD from HC, followed by gait variability measures. Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by increased response times leading to a variety of biomechanical symptoms, such as tremors, stooping, and gait instability. Although the deterioration in biomechanical control can intuitively be related to sluggish response times, how the delay leads to such biomechanical symptoms as tremor is not yet understood. Only recently has it been explained from the perspective of feedback control theory that delay beyond a threshold can be the cause of Parkinsonian tremor (Palanthandalam-Madapusi and Goyal, 2011, “Is Parkinsonian Tremor a Limit Cycle?” J. Mech. Med. Biol., 11(5), pp. 1017–1023). The present paper correlates several observations from this perspective to clinical facts and reinforces them with simple numerical and experimental examples. Thus, the present work provides a framework toward developing a deeper conceptual understanding of the mechanism behind PD symptoms. Furthermore, it lays a foundation for developing tools for diagnosis and progress tracking of the disease by identifying some key trends.