A Contact-Free Optical Device for the Detection of Pulmonary Congestion-A Pilot Study.

BACKGROUND: The cost of heart failure hospitalizations in the US alone is over USD 10 billion per year. Over 4 million Americans are hospitalized every year due to heart failure (HF), with a median length of stay of 4 days and an in-hospital mortality rate that exceeds 5%. Hospitalizations of patients with HF can be prevented by early detection of lung congestion. Our study assessed a new contact-free optical medical device used for the early detection of lung congestion. METHODS: The Gili system is an FDA-cleared device used for measuring chest motion vibration data. Lung congestion in the study was assessed clinically and verified via two cardiologists. An algorithm was developed using machine learning techniques, and cross-validation of the findings was performed to estimate the accuracy of the algorithm. RESULTS: A total of 227 patients were recruited (101 cases vs. 126 controls). The sensitivity and specificity for the device in our study were 0.91 (95% CI: 0.86-0.93) and 0.91 (95% CI: 0.87-0.94), respectively. In all instances, the observed estimates of PPVs and NPVs were at least 0.82 and 0.90, respectively. The accuracy of the algorithm was not affected by different covariates (including respiratory or valvular conditions). CONCLUSIONS: This study demonstrates the efficacy of a contact-free optical device for detecting lung congestion. Further validation of the study results across a larger and precise scale is warranted.

SPARC: a new approach to quantifying gait smoothness in patients with Parkinson's disease.

BACKGROUND: Impairments in biomechanics and neural control can disrupt the timing and muscle pattern activation necessary for smooth gait. Gait is one of the most affected motor characteristics in Parkinson's disease (PD), but its smoothness has not been well-studied. This work applies the recently proposed spectral arc length measure (SPARC) to study, for the first time, gait in patients with PD. We hypothesized that the gait of patients with PD would be less smooth than that of healthy controls, as reflected in the SPARC measures. METHODS: The gait of 101 PD patients and 39 healthy controls was assessed using an inertial sensor. Smoothness of gait was estimated with SPARC (respectively from acceleration and angular velocity signals, SPARC-Acc and SPARC-Gyro) and harmonic ratios. Correlations between SPARC, traditional gait measures and the motor part of the Unified Parkinson's Disease Rating Scale (UPDRS) were evaluated. Measurements and analysis were conducted with and without anti-PD medication. RESULTS: SPARC measures were lower (less smooth) in PD than in controls (SPARC-Acc: PD: - 6.11 ± 0.74; CO: -5.17 ± 0.79; p <  0.001). When comparing PD to controls, SPARC-Acc differed more than other measures of gait (i.e., largest effect size, which was > 1). SPARC measures were correlated with UPDRS motor score (r = - 0.65), while they were independent of other measures of gait smoothness. PD gait in the on state was smoother than in the off state (p <  0.001). CONCLUSIONS: SPARC calculated from trunk acceleration and angular velocity signals provide valid measures of walking smoothness in PD. SPARC is sensitive to Parkinson's disease and PD medications and can be used of as another, complementary measure of the motor control of walking in PD.

Treadmill walking reduces pre-frontal activation in patients with Parkinson's disease.

BACKGROUND: Among patients with Parkinson's disease (PD), gait is typically disturbed and less automatic. These gait changes are associated with impaired rhythmicity and increased prefrontal activation, presumably in an attempt to compensate for reduced automaticity. RESEARCH QUESTION: We investigated whether during treadmill walking, when the pace is determined and fixed, prefrontal activation in patients with PD is lower, as compared to over-ground walking. METHODS: Twenty patients with PD (age: 69.8 ±â€¯6.5 yrs.; MoCA: 26.9 ±â€¯2.4; disease duration: 7.9 ±â€¯4.2 yrs) walked at a self-selected walking speed over-ground and on a treadmill. A wireless functional near infrared spectroscopy (fNIRS) system measured prefrontal lobe activation, i.e., oxygenated hemoglobin (Hb02) in the pre-frontal area. Gait was evaluated using 3D-accelerometers attached to the lower back and ankles (Opal™, APDM). Dynamic gait stability was assessed using the maximum Lyapunov exponent to investigate automaticity of the walking pattern. RESULTS: Hb02 was lower during treadmill walking than during over-ground walking (p = 0.001). Gait stability was greater on the treadmill, compared to over-ground walking, in both the anteroposterior and medio-lateral axes (p < 0.001). SIGNIFICANCE: These findings support the notion that when gait is externally paced, prefrontal lobe activation is reduced in patients with PD, perhaps reflecting a reduced need for compensatory cognitive mechanisms.

A comparison study of local dynamic stability measures of daily life walking in older adult community-dwelling fallers and non-fallers.

In the present study we compared the performance of three different estimations of local dynamic stability λ to distinguish between the dynamics of the daily-life walking of elderly fallers and non-fallers. The study re-analyses inertial sensor data of 3-days daily-life activity originally described by Weiss et al. (2013). The data set contains inertial sensor data from 39 older persons who reported less than 2 falls and 31 older persons who reported two or more falls the previous year. 3D-acceleration and 3D-velocity signals from walking epochs of 50s were used to reconstruct a state space using three different methods. Local dynamic stability was estimated with the algorithms proposed by Rosenstein et al. (1993), Kantz (1994), and Ihlen et al. (2012a). Median λs assessed by Ihlen׳s and Kantz׳ algorithms discriminated better between elderly fallers and non-fallers (highest AUC=0.75 and 0.73) than Rosenstein׳s algorithm (highest AUC=0.59). The present results suggest that the ability of λ to distinguish between fallers and non-fallers is dependent on the parameter setting of the chosen algorithm. Further replication in larger samples of community-dwelling older persons and different patient groups is necessary before including the suggested parameter settings in fall risk assessment and prediction models.