Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG) that most effectively distinguished performance differences across age (age 18-75). Second, we determined the discriminative ability of those identified variables to classify a younger (age 18-45) and older age group (age 46-75). From healthy adults (n = 59), trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS) model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA) assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical practice

Task specificity and neural adaptations after balance learning in young adults

Background: Only 30 min of balance skill training can significantly improve behavioral and neuromuscular outcomes. However, it is unclear if such a rapidly acquired skill is also retained and transferred to other untrained balance tasks.Research question: What are the effects of a single balance training session on balance skill acquisition, retention, and transferability and on measures of neural plasticity examined by transcranial magnetic brain stimulation (TMS) and inter-muscular coherence?Methods: Healthy younger adults (n = 36, age 20.9, 18 M) were randomly assigned to: Balance training (BT); Active control (cycling training, CT) or non-active control (NC) and received a 20min intervention. Before, immediately and similar to 7 days after the interventions, we assessed performance in the trained wobble board task, untrained static standing tasks and dynamic beam walking balance tasks. Underlying neural plasticity was assessed by tibialis anterior motor evoked potential, intracortical facilitation, short-interval intracortical inhibition and long-interval intracortical inhibition using TMS and by inter-muscular coherence.Results: BT, but not CT (18%, d = 0.32) or NC (-1%, d = -0.02), improved balance performance in the trained, wobble board task by 207% (effect size d = 2.12). BT retained the acquired skill after a 1-week no-training period (136%, d = 1.57). No changes occurred in 4 measures of balance beam walking, in 8 measures of static balance, in 8 measures of intermuscular coherence, and in 4 TMS measures of supra-spinal plasticity (all p &gt; 0.05).Significance: Healthy young adults can learn a specific balance skill very rapidly but one should be aware that while such improvements were retained, the magnitude of transfer (32%, d = 0.94) to other balancing skills was statistically not significant. Additional studies are needed to determine the underlying neural mechanisms of rapid balance skill acquisition, retention, and transfer.</p

Can We Identify Subgroups of Patients with Chronic Low Back Pain Based on Motor Variability? A Systematic Scoping Review

The identification of homogeneous subgroups of patients with chronic low back pain (CLBP), based on distinct patterns of motor control, could support the tailoring of therapy and improve the effectiveness of rehabilitation. The purpose of this review was (1) to assess if there are differences in motor variability between patients with CLBP and pain-free controls, as well as inter-individually among patients with CLBP, during the performance of functional tasks; and (2) to examine the relationship between motor variability and CLBP across time. A literature search was conducted on the electronic databases Pubmed, EMBASE, and Web of Science, including papers published any time up to September 2021. Two reviewers independently screened the search results, assessed the risk of bias, and extracted the data. Twenty-two cross-sectional and three longitudinal studies investigating motor variability during functional tasks were examined. There are differences in motor variability between patients with CLBP and pain-free controls during the performance of functional tasks, albeit with discrepant results between tasks and among studies. The longitudinal studies revealed the persistence of motor control changes following interventions, but the relationship between changes in motor variability and reduction in pain intensity was inconclusive. Based on the reviewed literature, no stratification of homogeneous subgroups into distinct patterns of motor variability in the CLBP population could be made. Studies diverged in methodologies and theoretical frameworks and in metrics used to assess and interpret motor variability. In the future, more large-sample studies, including longitudinal designs, are needed, with standardized metrics that quantify motor variability to fill the identified evidence gaps

Comparison of hospital-wide and unit-specific cumulative antibiograms in hospital- and community-acquired infection

Background: Empirical antibacterial therapy in hospitals is usually guided by local epidemiologic features reflected by institutional cumulative antibiograms. We investigated additional information inferred by aggregating cumulative antibiograms by type of unit or according to the place of acquisition (i.e. community vs. hospital) of the bacteria. Materials and methods: Antimicrobial susceptibility rates of selected pathogens were collected over a 4-year period in an university-affiliated hospital. Hospital-wide antibiograms were compared with those selected by type of unit and sampling time (48h after hospital admission). Results: Strains isolated >48h after admission were less susceptible than those presumably arising from the community (48h after admission. When compared to hospital-wide antibiograms, susceptibility rates were lower in the ICU and surgical units for Escherichia coli to amoxicillin-clavulanate, enterococci to penicillin, and Pseudomonas aeruginosa to anti-pseudomonal beta-lactams, and in medical units for Staphylococcus aureus to oxacillin. In contrast, few differences were observed among strains isolated within 48h of admission. Conclusions: Hospital-wide antibiograms reflect the susceptibility pattern for a specific unit with respect to community-acquired, but not to hospital-acquired strains. Antibiograms adjusted to these parameters may be useful in guiding the choice of empirical antibacterial therap

Future challenges and chances in the diagnosis and management of invasive mould infections in cancer patients.

Diagnosis, treatment, and management of invasive mould infections (IMI) are challenged by several risk factors, including local epidemiological characteristics, the emergence of fungal resistance and the innate resistance of emerging pathogens, the use of new immunosuppressants, as well as off-target effects of new oncological drugs. The presence of specific host genetic variants and the patient's immune system status may also influence the establishment of an IMI and the outcome of its therapy. Immunological components can thus be expected to play a pivotal role not only in the risk assessment and diagnosis, but also in the treatment of IMI. Cytokines could improve the reliability of an invasive aspergillosis diagnosis by serving as biomarkers as do serological and molecular assays, since they can be easily measured, and the turnaround time is short. The use of immunological markers in the assessment of treatment response could be helpful to reduce overtreatment in high risk patients and allow prompt escalation of antifungal treatment. Mould-active prophylaxis could be better targeted to individual host needs, leading to a targeted prophylaxis in patients with known immunological profiles associated with high susceptibility for IMI, in particular invasive aspergillosis. The alteration of cellular antifungal immune response through oncological drugs and immunosuppressants heavily influences the outcome and may be even more important than the choice of the antifungal treatment. There is a need for the development of new antifungal strategies, including individualized approaches for prevention and treatment of IMI that consider genetic traits of the patients. Anticancer and immunosuppressive drugs may alter the ability of the immune system to fight invasive mould infections and may be more important than the choice of the antifungal treatment. Individualized approaches for prevention and treatment of invasive mold infections are needed

Effects of attention on the control of locomotion in individuals with chronic low back pain

<p>Abstract</p> <p>Background</p> <p>People who suffer from low back pain (LBP) exhibit an abnormal gait pattern, characterized by shorter stride length, greater step width, and an impaired thorax-pelvis coordination which may undermine functional walking. As a result, gait in LBP may require stronger cognitive regulation compared to pain free subjects thereby affecting the degree of automaticity of gait control. Conversely, because chronic pain has a strong attentional component, diverting attention away from the pain might facilitate a more efficient walking pattern.</p> <p>Methods</p> <p>Twelve individuals with LBP and fourteen controls participated. Subjects walked on a treadmill at comfortable speed, under varying conditions of attentional load: (a) no secondary task, (b) naming the colors of squares on a screen, (c) naming the colors of color words ("color Stroop task"), and (d) naming the colors of words depicting motor activities. Markers were attached to the thorax, pelvis and feet. Motion was recorded using a three-camera SIMI system with a sample frequency of 100 Hz. To examine the effects of health status and attention on gait, mean and variability of stride parameters were calculated. The coordination between thoracic and pelvic rotations was quantified through the mean and variability of the relative phase between those oscillations.</p> <p>Results</p> <p>LBP sufferers had a lower walking speed, and consequently a smaller stride length and lower mean thorax-pelvis relative phase. Stride length variability was significantly lower in the LBP group but no significant effect of attention was observed. In both groups gait adaptations were found under performance of an attention demanding task, but significantly more so in individuals with LBP as indicated by an interaction effect on relative phase variability.</p> <p>Conclusion</p> <p>Gait in LBP sufferers was characterized by less variable upper body movements. The diminished flexibility in trunk coordination was aggravated under the influence of an attention demanding task. This provides further evidence that individuals with LBP tighten their gait control, and this suggests a stronger cognitive regulation of gait coordination in LBP. These changes in gait coordination reduce the capability to deal with unexpected perturbations, and are therefore maladaptive.</p

The detection of age groups by dynamic gait outcomes using machine learning approaches

Prevalence of gait impairments increases with age and is associated with mobility decline, fall risk and loss of independence. For geriatric patients, the risk of having gait disorders is even higher. Consequently, gait assessment in the clinics has become increasingly important. The purpose of the present study was to classify healthy young-middle aged, older adults and geriatric patients based on dynamic gait outcomes. Classification performance of three supervised machine learning methods was compared. From trunk 3D-accelerations of 239 subjects obtained during walking, 23 dynamic gait outcomes were calculated. Kernel Principal Component Analysis (KPCA) was applied for dimensionality reduction of the data for Support Vector Machine (SVM) classification. Random Forest (RF) and Artificial Neural Network (ANN) were applied to the 23 gait outcomes without prior data reduction. Classification accuracy of SVM was 89%, RF accuracy was 73%, and ANN accuracy was 90%. Gait outcomes that significantly contributed to classification included: Root Mean Square (Anterior-Posterior, Vertical), Cross Entropy (Medio-Lateral, Vertical), Lyapunov Exponent (Vertical), step regularity (Vertical) and gait speed. ANN is preferable due to the automated data reduction and significant gait outcome identification. For clinicians, these gait outcomes could be used for diagnosing subjects with mobility disabilities, fall risk and to monitor interventions. (This work was supported by Keep Control project, funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 721577.

Shotgun approaches to gait analysis:insights &amp; limitations

Background: Identifying features for gait classification is a formidable problem. The number of candidate measures is legion. This calls for proper, objective criteria when ranking their relevance.Methods: Following a shotgun approach we determined a plenitude of kinematic and physiological gait measures and ranked their relevance using conventional analysis of variance (ANOVA) supplemented by logistic and partial least squares (PLS) regressions. We illustrated this approach using data from two studies involving stroke patients, amputees, and healthy controls.Results: Only a handful of measures turned out significant in the ANOVAs. The logistic regressions, by contrast, revealed various measures that clearly discriminated between experimental groups and conditions. The PLS regression also identified several discriminating measures, but they did not always agree with those of the logistic regression.Discussion &amp; conclusion: Extracting a measure's classification capacity cannot solely rely on its statistical validity but typically requires proper post-hoc analysis. However, choosing the latter inevitably introduces some arbitrariness, which may affect outcome in general. We hence advocate the use of generic expert systems, possibly based on machine-learning.</p