81 research outputs found
Cognitive dual-task cost depends on the complexity of the cognitive task, but not on age and disease
No full textINTRODUCTION: Dual-tasking (DT) while walking is common in daily life and can affect both gait and cognitive performance depending on age, attention prioritization, task complexity and medical condition. The aim of the present study was to investigate the effects of DT on cognitive DT cost (DTC) (i) in a dataset including participants of different age groups, with different neurological disorders and chronic low-back pain (cLBP) (ii) at different levels of cognitive task complexity, and (iii) in the context of a setting relevant to daily life, such as combined straight walking and turning. MATERIALS AND METHODS: Ninety-one participants including healthy younger and older participants and patients with Parkinson's disease, Multiple Sclerosis, Stroke and cLBP performed a simple reaction time (SRT) task and three numerical Stroop tasks under the conditions congruent (StC), neutral (StN) and incongruent (StI). The tasks were performed both standing (single task, ST) and walking (DT), and DTC was calculated. Mixed ANOVAs were used to determine the effect of group and task complexity on cognitive DTC. RESULTS: A longer response time in DT than in ST was observed during SRT. However, the response time was shorter in DT during StI. DTC decreased with increasing complexity of the cognitive task. There was no significant effect of age and group on cognitive DTC. CONCLUSION: Our results suggest that regardless of age and disease group, simple cognitive tasks show the largest and most stable cognitive effects during DT. This may be relevant to the design of future observational studies, clinical trials and for clinical routine
Changes in Coordination and Its Variability with an Increase in Functional Performance of the Lower Extremities
No full textClinical gait analysis has a long-standing tradition in biomechanics. However, the use of kinematic data or segment coordination has not been reported based on wearable sensors in “real-life” environments. In this work, the skeletal kinematics of 21 healthy and 24 neurogeriatric participants was collected in a magnetically disturbed environment with inertial measurement units (IMUs) using an accelerometer-based functional calibration method. The system consists of seven IMUs attached to the lower back, the thighs, the shanks, and the feet to acquire and process the raw sensor data. The Short Physical Performance Battery (SPPB) test was performed to relate joint kinematics and segment coordination to the overall SPPB score. Participants were then divided into three subgroups based on low (0–6), moderate (7–9), or high (10–12) SPPB scores. The main finding of this study is that most IMU-based parameters significantly correlated with the SPPB score and the parameters significantly differed between the SPPB subgroups. Lower limb range of motion and joint segment coordination correlated positively with the SPPB score, and the segment coordination variability correlated negatively. The results suggest that segment coordination impairments become more pronounced with a decreasing SPPB score, indicating that participants with low overall SPPB scores produce a peculiar inconsistent walking pattern to counteract lower extremity impairment in strength, balance, and mobility. Our findings confirm the usefulness of SPPB through objectively measured parameters, which may be relevant for the design of future studies and clinical routines
Unsupervised but not supervised gait parameters are related to fatigue in Parkinson's disease: a pilot study
No full textIntroduction: Fatigue is a common and disabling symptom in Parkinson's disease (PD), also affecting gait. Detection of fatigue-associated changes of gait using mobile health technologies (MHT) could become increasingly effective.Methods: Cognitively unimpaired PD patients without fluctuations (UPDRS IV < 1) underwent a standard neurological assessment including the PD-Fatigue scale (PFS-16). PD patients with (PD-F) and without fatigue (PD-N) were matched for age, sex, cognitive function and disease severity. Each participant underwent MHT gait assessment under supervised condition (SC) and unsupervised condition (UC).Results: Gait parameters of 21 PD-F and 21 PD-N did not significantly differ under SC. Under UC, PD-F showed higher step time, step time variability and asymmetry index compared to PD-N and the PFS-16 correlated with step time.Conclusion: This is the first MHT-based study with PD patients showing a correlation between fatigue and gait parameters. In addition, the data collected suggest that UC is clearly superior to SC in addressing this question
The loss of personal privacy and its consequences for social research
No full textThis article chronicles more than 30 years of public opinion, politics, and law and policy on privacy and confidentiality that have had far-reaching consequences for access by the social research community to administrative and statistical records produced by government. A hostile political environment, public controversy over the decennial census long form, media coverage, and public fears about the vast accumulations of personal information by the private sector were catalysts for a recent proposal by the U.S. Bureau of the Census that would have significantly altered the contents of the 2000 census Public Use Microdata Sample (PUMS). These events show clearly that science does not operate independently from the political sphere but may be transformed by a political world where powerful interests lead government agencies to assume responsibility for privacy protection that can result in reducing access to statistical data
Turning When Using Smartphone in Persons With and Those Without Neurologic Conditions: Observational Study
No full textBackground:
Turning during walking is a relevant and common everyday movement and it depends on a correct top-down intersegmental coordination. This could be reduced in several conditions (en bloc turning), and an altered turning kinematics has been linked to increased risk of falls. Smartphone use has been associated with poorer balance and gait; however, its effect on turning-while-walking has not been investigated yet. This study explores turning intersegmental coordination during smartphone use in different age groups and neurologic conditions.
Objective:
This study aims to evaluate the effect of smartphone use on turning behavior in healthy individuals of different ages and those with various neurological diseases.
Methods:
Younger (aged 18-60 years) and older (aged >60 years) healthy individuals and those with Parkinson disease, multiple sclerosis, subacute stroke (<4 weeks), or lower-back pain performed turning-while-walking alone (single task [ST]) and while performing 2 different cognitive tasks of increasing complexity (dual task [DT]). The mobility task consisted of walking up and down a 5-m walkway at self-selected speed, thus including 180° turns. Cognitive tasks consisted of a simple reaction time test (simple DT [SDT]) and a numerical Stroop test (complex DT [CDT]). General (turn duration and the number of steps while turning), segmental (peak angular velocity), and intersegmental turning parameters (intersegmental turning onset latency and maximum intersegmental angle) were extracted for head, sternum, and pelvis using a motion capture system and a turning detection algorithm.
Results:
In total, 121 participants were enrolled. All participants, irrespective of age and neurologic disease, showed a reduced intersegmental turning onset latency and a reduced maximum intersegmental angle of both pelvis and sternum relative to head, thus indicating an en bloc turning behavior when using a smartphone. With regard to change from the ST to turning when using a smartphone, participants with Parkinson disease reduced their peak angular velocity the most, which was significantly different from lower-back pain relative to the head (P<.01). Participants with stroke showed en bloc turning already without smartphone use.
Conclusions:
Smartphone use during turning-while-walking may lead to en bloc turning and thus increase fall risk across age and neurologic disease groups. This behavior is probably particularly dangerous for those groups with the most pronounced changes in turning parameters during smartphone use and the highest fall risk, such as individuals with Parkinson disease. Moreover, the experimental paradigm presented here might be useful in differentiating individuals with lower-back pain without and those with early or prodromal Parkinson disease. In individuals with subacute stroke, en bloc turning could represent a compensative strategy to overcome the newly occurring mobility deficit. Considering the ubiquitous smartphone use in daily life, this study should stimulate future studies in the area of fall risk and neurological and orthopedic diseases
Social scientists at work on the electronic network
No full textThe purpose of this article is to contribute to our stock of knowledge about who uses networks, how they are used, and what contribution the networks make to advancing the scientific enterprise. Between 1985 and 1990, the Survey of Income and Program Participation (SIPP) ACCESS data facility at the University of Wisconsin-Madison provided social scientists in the United States and elsewhere with access through the electronic networks to complex and dynamic statistical data; the 1984 SIPP is a longitudinal panel survey designed to examine economic well-being in the United States. This article describes the conceptual framework and design of SIPP ACCESS; examines how network users communicated with the SIPP ACCESS profect staff about the SIPP data; and evaluates one outcome derived from the communications, the improvement of the quality of the SIPP data. The direct and indirect benefits to social scientists of electronic networks are discussed. The author concludes with a series of policy recommendations that link the assessment of our inadequate knowledge base for evaluating how electronic networks advance the scientific enterprise and the SIPP ACCESS research network experience to the policy initiatives of the High Performance Computing Act of 1991 (P.L. 102-194) and the related extensive recommendations embodied in Grand Challenges 1993 High Performance Computing and Communications (The FY 1993 U.S. Research and Development Program)
Inertial Measurement Unit-Based Gait Event Detection in Healthy and Neurological Cohorts: A Walk in the Dark
No full textA deep learning (DL)-based network is developed to determine gait events from IMU data from a shank- or foot-worn device. The DL network takes as input the raw IMU data and predicts for each time step the probability that it corresponds to an initial or final contact. The algorithm is validated for walking at different self-selected speeds across multiple neurological diseases and both in clinical research settings and the habitual environment. The algorithms shows a high detection rate for initial and final contacts, and a small time error when compared to reference events obtained with an optical motion capture system or pressure insoles. Based on the excellent performance, it is concluded that the DL algorithm is well suited for continuous long-term monitoring of gait in the habitual environment
Seeking explanation in theory: Reflections on the social practices of organizations that distribute public use microdata files for research purposes
No full textPublic concern about personal privacy has recently focused on issues of Internet data security and personal information as big business. The scientific discourse about information privacy focuses on the cross-pressures of maintaining confidentiality and ensuring access in the context of the production of statistical data for public policy and social research and the associated technical solutions for releasing statistical data. This article reports some of the key findings from a small-scale survey of organizational practices to limit disclosure of confidential information prior to publishing public use microdata files, and illustrates how the rules for preserving confidentiality were applied in practice. Explanation for the apparent deficits and wide variations in the extent of knowledge about statistical disclosure limitation (SDL) methods is located in theories of organizational life and communities of practice. The article concludes with suggestions for improving communication between communities of practice to enhance the knowledge base of those responsible for producing public use microdata files
A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts
No full textMany algorithms use 3D accelerometer and/or gyroscope data from inertial measurement
unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms
often require knowledge about sensor orientation and use empirically derived thresholds. As align ment cannot always be controlled for in ambulatory assessments, methods are needed that require
little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep
learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked
5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on
the left and right ankle and shank. Gait events were detected and stride parameters were extracted
using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The
deep learning model consisted of convolutional layers using dilated convolutions, followed by two
independent fully connected layers to predict whether a time step corresponded to the event of
initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both
initial and final contacts across sensor locations (recall ≥ 92%, precision ≥ 97%). Time agreement
was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile
range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters
derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum
limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach
was considered a valid approach for detecting gait events and extracting stride-specific parameters
with little knowledge on exact IMU location and orientation in conditions with and without walking
pathologies due to neurological diseases
Full-Body Mobility Data to Validate Inertial Measurement Unit Algorithms in Healthy and Neurological Cohorts
No full textGait and balance dysfunctions are common in neurological disorders and have a negative effect on quality of life. Regularly quantifying these mobility limitations can be used to measure disease progression and the effect of treatment. This information can be used to provide a more individualized treatment. Inertial measurement units (IMUs) can be utilized to quantify mobility in different contexts. However, algorithms are required to extract valuable parameters out of the raw IMU data. These algorithms need to be validated to make sure that they extract the features they should extract. This validation should be performed per disease since different mobility limitations or symptoms can influence the performance of an algorithm in different ways. Therefore, this dataset contains data from both healthy subjects and patients with neurological diseases (Parkinson’s disease, stroke, multiple sclerosis, chronic low back pain). The full bodies of 167 subjects were measured with IMUs and an optical motion capture (reference) system. Subjects performed multiple standardized mobility assessments and non-standardized activities of daily living. The data of 21 healthy subjects are shared online, data of the other subjects and patients can only be obtained after contacting the corresponding author and signing a data sharing agreement
- …
