Disentangling the effects of metabolic cost and accuracy on movement speed.

On any given day, we make countless reaching movements to objects around us. While such ubiquity may suggest uniformity, each movement's speed is unique-why is this? Reach speed is known to be influenced by accuracy; we slow down to sustain high accuracy. However, in other forms of movement like walking or running, metabolic cost is often the primary determinant of movement speed. Here we bridge this gap and ask: how do metabolic cost and accuracy interact to determine speed of reaching movements? First, we systematically measure the effect of increasing mass on the metabolic cost of reaching across a range of movement speeds. Next, in a sequence of three experiments, we examine how added mass affects preferred reaching speed across changing accuracy requirements. We find that, while added mass consistently increases metabolic cost thereby leading to slower metabolically optimal movement speeds, self-selected reach speeds are slower than those predicted by an optimization of metabolic cost alone. We then demonstrate how a single model that considers both accuracy and metabolic costs can explain preferred movement speeds. Together, our findings provide a unifying framework to illuminate the combined effects of metabolic cost and accuracy on movement speed and highlight the integral role metabolic cost plays in determining reach speed.

Validation of Proprietary and Novel Step-counting Algorithms for Individuals Ambulating With a Lower Limb Prosthesis.

OBJECTIVE: To compare the accuracy and reliability of 10 different accelerometer-based step-counting algorithms for individuals with lower limb loss, accounting for different clinical characteristics and real-world activities. DESIGN: Cross-sectional study. SETTING: General community setting (ie, institutional research laboratory and community free-living). PARTICIPANTS: Forty-eight individuals with a lower limb amputation (N=48) wore an ActiGraph (AG) wGT3x-BT accelerometer proximal to the foot of their prosthetic limb during labeled indoor/outdoor activities and community free-living. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Intraclass correlation coefficient (ICC), absolute and root mean square error (RMSE), and Bland Altman plots were used to compare true (manual) step counts to estimated step counts from the proprietary AG Default algorithm and low frequency extension filter, as well as from 8 novel algorithms based on continuous wavelet transforms, fast Fourier transforms (FFTs), and peak detection. RESULTS: All algorithms had excellent agreement with manual step counts (ICC>0.9). The AG Default and FFT algorithms had the highest overall error (RMSE=17.81 and 19.91 steps, respectively), widest limits of agreement, and highest error during outdoor and ramp ambulation. The AG Default algorithm also had among the highest error during indoor ambulation and stairs, while a FFT algorithm had the highest error during stationary tasks. Peak detection algorithms, especially those using pre-set parameters with a trial-specific component, had among the lowest error across all activities (RMSE=4.07-8.99 steps). CONCLUSIONS: Because of its simplicity and accuracy across activities and clinical characteristics, we recommend the peak detection algorithm with set parameters to count steps using a prosthetic-worn AG among individuals with lower limb loss for clinical and research applications.

Miniaturized wireless, skin-integrated sensor networks for quantifying full-body movement behaviors and vital signs in infants.

Early identification of atypical infant movement behaviors consistent with underlying neuromotor pathologies can expedite timely enrollment in therapeutic interventions that exploit inherent neuroplasticity to promote recovery. Traditional neuromotor assessments rely on qualitative evaluations performed by specially trained personnel, mostly available in tertiary medical centers or specialized facilities. Such approaches are high in cost, require geographic proximity to advanced healthcare resources, and yield mostly qualitative insight. This paper introduces a simple, low-cost alternative in the form of a technology customized for quantitatively capturing continuous, full-body kinematics of infants during free living conditions at home or in clinical settings while simultaneously recording essential vital signs data. The system consists of a wireless network of small, flexible inertial sensors placed at strategic locations across the body and operated in a wide-bandwidth and time-synchronized fashion. The data serve as the basis for reconstructing three-dimensional motions in avatar form without the need for video recordings and associated privacy concerns, for remote visual assessments by experts. These quantitative measurements can also be presented in graphical format and analyzed with machine-learning techniques, with potential to automate and systematize traditional motor assessments. Clinical implementations with infants at low and at elevated risks for atypical neuromotor development illustrates application of this system in quantitative and semiquantitative assessments of patterns of gross motor skills, along with body temperature, heart rate, and respiratory rate, from long-term and follow-up measurements over a 3-mo period following birth. The engineering aspects are compatible for scaled deployment, with the potential to improve health outcomes for children worldwide via early, pragmatic detection methods.

Unmet needs in uncomplicated urinary tract infection in the United States and Germany: a physician survey.

BACKGROUND: Uncomplicated urinary tract infections (uUTIs/acute cystitis) are among the most common infections in women worldwide. There are differences in uUTI treatment guidelines between countries and understanding the needs of physicians in diverse healthcare systems is important for developing new treatments. We performed a survey of physicians in the United States (US) and Germany to understand their perceptions of, and management approaches to uUTI. METHODS: This was a cross-sectional online survey of physicians in the US and Germany who were actively treating patients with uUTI (≥ 10 patients/month). Physicians were recruited via a specialist panel and the survey was piloted with 2 physicians (1 US, 1 Germany) prior to study commencement. Data were analyzed with descriptive statistics. RESULTS: A total of 300 physicians were surveyed (n = 200 US, n = 100 Germany). Across countries and specialties, physicians estimated 16-43% of patients did not receive complete relief from initial therapy and 33-37% had recurrent infections. Urine culture and susceptibility testing was more common in the US and among urologists. The most commonly selected first-line therapy was trimethoprim-sulfamethoxazole in the US (76%) and fosfomycin in Germany (61%). Ciprofloxacin was the most selected following multiple treatment failures (51% US, 45% Germany). Overall, 35% of US and 45% of German physicians agreed with the statement "I feel there is a good selection of treatment options" and ≥ 50% felt that current treatments provided good symptom relief. More than 90% of physicians included symptom relief amongst their top 3 treatment goals. The overall impact of symptoms on patients' lives was rated "a great deal" by 51% of US and 38% of German physicians, increasing with each treatment failure. Most physicians (> 80%) agreed that antimicrobial resistance (AMR) is serious, but fewer (56% US, 46% Germany) had a high level of confidence in their knowledge of AMR. CONCLUSIONS: Treatment goals for uUTI were similar in the US and Germany, although with nuances to disease management approaches. Physicians recognized that treatment failures have a significant impact on patients' lives and that AMR is a serious problem, though many did not have confidence in their own knowledge of AMR.

Examination of physical activity development in early childhood: protocol for a longitudinal cohort study of mother-toddler dyads.

BACKGROUND: Physical activity (PA) development in toddlers (age 1 and 2 years) is not well understood, partly because of a lack of analytic tools for accelerometer-based data processing that can accurately evaluate PA among toddlers. This has led to a knowledge gap regarding how parenting practices around PA, mothers' PA level, mothers' parenting stress, and child developmental and behavioral problems influence PA development in early childhood. METHODS: The Child and Mother Physical Activity Study is a longitudinal study to observe PA development in toddlerhood and examine the influence of personal and parental characteristics on PA development. The study is designed to refine and validate an accelerometer-based machine learning algorithm for toddler activity recognition (Aim 1), apply the algorithm to compare the trajectories of toddler PA levels in males and females age 1-3 years (Aim 2), and explore the association between gross motor development and PA development in toddlerhood, as well as how parenting practices around PA, mothers' PA, mothers' parenting stress, and child developmental and behavioral problems are associated with toddlerhood PA development (Exploratory Aims 3a-c). DISCUSSION: This study will be one of the first to use longitudinal data to validate a machine learning activity recognition algorithm and apply the algorithm to quantify free-living ambulatory movement in toddlers. The study findings will help fill a significant methodological gap in toddler PA measurement and expand the body of knowledge on the factors influencing early childhood PA development.

Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors.

Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this study, we explored the feasibility of (1) collecting overnight biophysical data from patients with subacute stroke using a simple sensor system and (2) constructing machine-learned algorithms to detect sleep stages. Ten individuals with stroke in an inpatient rehabilitation hospital wore two wireless sensors during a single night of sleep. Polysomnography served as ground truth to classify different sleep stages. A population model, trained on data from multiple patients and tested on data from a separate patient, performed poorly for this limited sample. Personal models trained on data from one patient and tested on separate data from the same patient demonstrated markedly improved performance over population models and research-grade wearable devices to detect sleep/wake. Ultimately, the heterogeneity of biophysical signals after stroke may present a challenge in building generalizable population models. Personal models offer a provisional method to capture high-resolution sleep metrics from simple wearable sensors by leveraging a single night of polysomnography data.

Meckel's Masquerade: Penetrating the Disguise.

ABSTRACT: Children with abdominal pain are frequently seen in emergency departments. Physicians and parents worry about appendicitis; physicians are also concerned about intussusception and bowel obstruction in patients with previous surgical procedures. Sometimes the patient is ill, and the diagnosis is elusive. In a 6-month period at our pediatric emergency department with an annual census of 57,400 patients, we cared for 4 patients who presented with abdominal pain due to complications of Meckel's diverticulum. None presented with painless rectal bleeding, the complication of which physicians are most aware. We are reporting these patients to raise awareness of Meckel's diverticulum as a cause of acute abdomen in children. Meckel's diverticulum may masquerade as appendicitis, an abdominal mass, intussusception, or a complication of severe constipation.

P2X Antagonists Inhibit HIV-1 Productive Infection and Inflammatory Cytokines Interleukin-10 (IL-10) and IL-1ß in a Human Tonsil Explant Model.

HIV-1 causes a persistent infection of the immune system that is associated with chronic comorbidities. The mechanisms that underlie this inflammation are poorly understood. Emerging literature has implicated proinflammatory purinergic receptors and downstream signaling mediators in HIV-1 infection. This study probed whether inhibitors of purinergic receptors would reduce HIV-1 infection and HIV-1-stimulated inflammation. An ex vivo human tonsil histoculture infection model was developed to support HIV-1 productive infection and stimulated the inflammatory cytokine interleukin-1 beta (IL-1ß) and the immunosuppressive cytokine interleukin-10 (IL-10). This study tests whether inhibitors of purinergic receptors would reduce HIV-1 infection and HIV-1-stimulated inflammation. The purinergic P2X1 receptor antagonist NF449, the purinergic P2X7 receptor antagonist A438079, and azidothymidine (AZT) were tested in HIV-1-infected human tonsil explants to compare levels of inhibition of HIV-1 infection and HIV-stimulated inflammatory cytokine production. All drugs limited HIV-1 productive infection, but P2X-selective antagonists (NF449 and A438079) significantly lowered HIV-stimulated IL-10 and IL-1ß. We further observed that P2X1- and P2X7-selective antagonists can act differentially as inhibitors of both HIV-1 infection and HIV-1-stimulated inflammation. Our findings highlight the differential effects of HIV-1 on inflammation in peripheral blood compared to those in lymphoid tissue. For the first time, we demonstrate that P2X-selective antagonists act differentially as inhibitors of both HIV-1 infection and HIV-1-stimulated inflammation. Drugs that block these pathways can have independent inhibitory activities against HIV-1 infection and HIV-induced inflammation.IMPORTANCE Patients who are chronically infected with HIV-1 experience sequelae related to chronic inflammation. The mechanisms of this inflammation have not been elucidated. Here, we describe a class of drugs that target the P2X proinflammatory signaling receptors in a human tonsil explant model. This model highlights differences in HIV-1 stimulation of lymphoid tissue inflammation and peripheral blood. These drugs serve to block both HIV-1 infection and production of IL-10 and IL-1ß in lymphoid tissue, suggesting a novel approach to HIV-1 therapeutics in which both HIV-1 replication and inflammatory signaling are simultaneously targeted.

The impact of a yoga-based physical therapy group for individuals with traumatic brain injury: results from a pilot study.

OBJECTIVE: To compare the impacts of yoga-based physical therapy versus a seated rest within the context of standard rehabilitation practice on sleep, heart rate variability (HRV), anxiety, and fatigue during acute traumatic brain injury (TBI) rehabilitation. METHODS: Eleven individuals participated in this crossover study involving the following interventions in a randomized order: group yoga-based physical therapy (YPT), conventional physical therapy (CPT), and group seated rest in a relaxing environment (SR). HRV and self-reported anxiety and fatigue were measured immediately before and after each group, and sleep after each condition and at baseline. Data was analyzed using generalized linear mixed models with repeated measures. RESULTS: The interaction between time and treatment was statistically significant (p = .0203). For the SR treatment, wake after sleep onset (WASO) rate was reduced from 14.99 to 10.60 (IRR = 0.71; p = .006). Time and treatment were not found to be statistically significantly associated with any of the secondary outcomes. CONCLUSION: Yoga-based physical therapy is feasible and safe in the inpatient rehabilitation setting following TBI. Sleep quality improved following the addition of a one-hour seated rest in a relaxing environment to a standard rehabilitation daily schedule, suggesting that structured rest time may be beneficial to sleep hygiene during inpatient rehabilitation following TBI. ClinicalTrials.Gov Registration Number: NCT03701594.

Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach.

BACKGROUND: In clinical practice, therapists often rely on clinical outcome measures to quantify a patient's impairment and function. Predicting a patient's discharge outcome using baseline clinical information may help clinicians design more targeted treatment strategies and better anticipate the patient's assistive needs and discharge care plan. The objective of this study was to develop predictive models for four standardized clinical outcome measures (Functional Independence Measure, Ten-Meter Walk Test, Six-Minute Walk Test, Berg Balance Scale) during inpatient rehabilitation. METHODS: Fifty stroke survivors admitted to a United States inpatient rehabilitation hospital participated in this study. Predictors chosen for the clinical discharge scores included demographics, stroke characteristics, and scores of clinical tests at admission. We used the Pearson product-moment and Spearman's rank correlation coefficients to calculate correlations among clinical outcome measures and predictors, a cross-validated Lasso regression to develop predictive equations for discharge scores of each clinical outcome measure, and a Random Forest based permutation analysis to compare the relative importance of the predictors. RESULTS: The predictive equations explained 70-77% of the variance in discharge scores and resulted in a normalized error of 13-15% for predicting the outcomes of new patients. The most important predictors were clinical test scores at admission. Additional variables that affected the discharge score of at least one clinical outcome were time from stroke onset to rehabilitation admission, age, sex, body mass index, race, and diagnosis of dysphasia or speech impairment. CONCLUSIONS: The models presented in this study could help clinicians and researchers to predict the discharge scores of clinical outcomes for individuals enrolled in an inpatient stroke rehabilitation program that adheres to U.S. Medicare standards.

Role of data measurement characteristics in the accurate detection of Parkinson's disease symptoms using wearable sensors.

BACKGROUND: Parkinson's disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD. METHODS: Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis. RESULTS: First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p < 0.001) for tremor, but not bradykinesia (p > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set. CONCLUSIONS: Our findings demonstrate the ability to simplify measurement characteristics from body-worn sensors while maintaining performance in PD symptom detection. Understanding the trade-off between model performance and data resolution is crucial to design efficient, accurate wearable sensing systems. This approach may improve the feasibility of long-term, continuous, and real-time monitoring of PD symptoms by reducing computational burden on wearable devices.

Time from neuraxial anesthesia placement to delivery is inversely proportional to umbilical arterial cord pH at scheduled cesarean delivery.

BACKGROUND: Neuraxial block-related hypotension and maternal obesity contribute to uterine hypoperfusion and decreased umbilical arterial pH at cesarean delivery. Between the time of anesthesia placement and delivery, the fetus may be exposed to a hypoperfused uterine environment without surgeon awareness of fetal compromise. OBJECTIVE: We sought to evaluate neonatal umbilical arterial pH according to predelivery time intervals at scheduled term cesarean. STUDY DESIGN: We performed a retrospective cohort study of cesarean deliveries between September 2014 and February 2017. Singleton gestations undergoing scheduled cesarean delivery under spinal anesthesia between 37 and 41 weeks with a reassuring preoperative nonstress test were included. Time intervals between operative room entry, spinal anesthesia placement, skin incision, uterine incision, and delivery were calculated. The primary outcome was umbilical arterial pH. Demographic data, maternal blood pressures, predelivery time intervals, and delivery outcomes were analyzed according to umbilical arterial pH intervals of <7.0, 7.01-7.10, 7.11-7.20, 7.21-7.30, and >7.30. Umbilical cord gas analytes and neonatal outcomes were analyzed by spinal to delivery time. Stepwise linear regression was performed to identify predictors of decreasing umbilical arterial pH. Receiver-operator characteristic curves were calculated for spinal to delivery time and umbilical arterial pH <7.0 and 7.1. RESULTS: Among 527 included participants, median umbilical arterial pH was 7.27 [interquartile range, 7.23-7.29] and body mass index was 35 kg/m2 [interquartile range, 30-41]. Both maternal body mass index and hypotensive episodes increased with decreasing umbilical arterial pH (P <.001, P ≤ .02). All predelivery time intervals (operative room to delivery, spinal to skin, spinal to delivery, and uterine incision to delivery) increased as umbilical arterial pH interval decreased (P < .05 for all). In a stepwise linear regression, maternal body mass index, noncephalic presentation, spinal start to delivery interval, uterine incision to delivery interval, and maximum reduction in blood pressure from baseline were predictive of decreasing umbilical arterial pH after controlling for confounding variables (F [5,442] = 17.7, P = .0001], adjusted R2 of 0.157. When evaluated by spinal to delivery time, both umbilical arterial and venous pH and partial pressure of carbon dioxide decreased (P < .001 for all), but base deficit and neonatal outcomes were similar (P ≥ .7 for all). There were 2 cases of hypoxic-ischemic encephalopathy (0.38%). A receiver-operating characteristic curve demonstrated that a spinal start to delivery time greater than 27 minutes was associated with an umbilical arterial pH <7.1 (area under the curve, 0.74, 100% sensitivity, 21% specificity), and an interval greater than 30 minutes was associated with an umbilical arterial pH <7.0 (area under the curve, 0.80, 100% sensitivity, 33% specificity). CONCLUSION: Longer spinal-to-delivery and uterine incision-to-delivery time intervals were associated with decreasing umbilical arterial pH at scheduled term cesarean delivery. Efforts to minimize predelivery time following spinal placement could reduce the frequency of unanticipated neonatal acidemia.

In-Hospital Cardiac Arrest in the Cardiac Catheterization Laboratory: Effective Transition from an ICU- to CCU-Led Resuscitation Team.

OBJECTIVES: (1) To examine the incidence and outcomes of in-hospital cardiac arrests (IHCAs) in a large unselected patient population who underwent coronary angiography at a single tertiary academic center and (2) to evaluate a transitional change in which the cardiologist is positioned as the cardiopulmonary resuscitation (CPR) leader in the cardiac catheterization laboratory (CCL) at our local tertiary care institution. BACKGROUND: IHCA is a major public health concern with increased patient morbidity and mortality. A proportion of all IHCAs occurs in the CCL. Although in-hospital resuscitation teams are often led by an Intensive Care Unit- (ICU-) trained physician and house staff, little is known on the role of a cardiologist in this setting. METHODS: Between 2012 and 2016, a single-center retrospective cohort study was performed examining 63 adult patients (70 ± 10 years, 60% males) who suffered from a cardiac arrest in the CCL. The ICU-led IHCAs included 19 patients, and the Coronary Care Unit- (CCU-) led IHCAs included 44 patients. RESULTS: Acute coronary syndrome accounted for more than 50% of cardiac arrests in the CCL. Pulseless electrical activity was the most common rhythm requiring chest compression, and cardiogenic shock most frequently initiated a code blue response. No significant differences were observed between the ICU-led and CCU-led cardiac arrests in terms of hospital length of stay and 1-year survival rate. CONCLUSION: In the evolving field of Critical Care Cardiology, the transition from an ICU-led to a CCU-lead code blue team in the CCL setting may lead to similar short-term and long-term outcomes.

WITHDRAWN: Immediate Adaptations to Poststroke Walking Performance Using a Wearable Robotic Exoskeleton.

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Augmenting Clinical Outcome Measures of Gait and Balance with a Single Inertial Sensor in Age-Ranged Healthy Adults.

Gait and balance impairments are linked with reduced mobility and increased risk of falling. Wearable sensing technologies, such as inertial measurement units (IMUs), may augment clinical assessments by providing continuous, high-resolution data. This study tested and validated the utility of a single IMU to quantify gait and balance features during routine clinical outcome tests, and evaluated changes in sensor-derived measurements with age, sex, height, and weight. Age-ranged, healthy individuals (N = 49, 20-70 years) wore a lower back IMU during the 10 m walk test (10MWT), Timed Up and Go (TUG), and Berg Balance Scale (BBS). Spatiotemporal gait parameters computed from the sensor data were validated against gold standard measures, demonstrating excellent agreement for stance time, step time, gait velocity, and step count (intraclass correlation (ICC) > 0.90). There was good agreement for swing time (ICC = 0.78) and moderate agreement for step length (ICC = 0.68). A total of 184 features were calculated from the acceleration and angular velocity signals across these tests, 36 of which had significant correlations with age. This approach was also demonstrated for an individual with stroke, providing higher resolution information about balance, gait, and mobility than the clinical test scores alone. Leveraging mobility data from wireless, wearable sensors can help clinicians and patients more objectively pinpoint impairments, track progression, and set personalized goals during and after rehabilitation.

Economics of Inhaled Oxygen Use as an Acute Therapy for Cluster Headache in the United States of America.

BACKGROUND: Cluster headache (CH) is a primary headache disorder associated with low levels of diagnosis and high unmet medical need. The pain attacks, associated anxiety, and fear in anticipation of the attacks are extremely debilitating to a patient with CH. For acute therapy, treatment guidelines recommend inhalation of high flow oxygen during the period of an attack. However, the use of oxygen for treatment of CH remains largely underutilized. OBJECTIVES: The objectives of the study, which covered each of the US states, were to map the current market landscape of medical grade oxygen for use in CH and to develop a cost simulator based on a patient's needs and geography. METHODS: Desk research was undertaken to obtain price lists and product catalogs from wholesale and retail suppliers of medical grade oxygen across all US states. Base case scenarios for chronic and episodic forms of CH were assumed. A cost simulator was used to calculate the cost of oxygen use using inputs including the state in USA, tank size and price, exacerbations per year, duration of exacerbation, attacks per day, flow rate and duration of flow. Information was also collected to determine if healthcare insurers covered the costs of home oxygen use for CH. RESULTS: Out of the 42 US states where pricing information of medical grade oxygen was available from suppliers, in 38 states the annual cost of high-flow oxygen for a patient with episodic CH was estimated to be <$1000. In 39 states, the annual cost of high-flow oxygen for a patient with chronic CH was estimated to be <$5000. Most of the home oxygen suppliers were familiar with CH and stocked the special non-rebreather masks and regulators necessary for this condition. It was found that many of the private commercial healthcare insurance providers reimbursed the cost of oxygen use for CH. However, the US Centers for Medicare and Medicaid Services (CMS) maintains there is insufficient evidence for coverage and continues to deny coverage for US Medicare and Medicaid patients. CONCLUSIONS: Results from our study showed that the current costs for oxygen use as an acute therapy in CH are not prohibitively expensive for patients and healthcare insurance providers. Apart from CMS, many insurers do reimburse the cost of oxygen use for CH. Our study suggests that further research is needed to determine if a lack of physician awareness about treatments and ways to prescribe are barriers for patients to access the high-flow oxygen treatment.

Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting.

BACKGROUND: Smartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a stroke. Activity recognition (AR) systems are typically trained on movement data from healthy individuals collected in a laboratory setting. However, movement patterns change after a stroke (eg, gait impairment), and activities may be performed differently at home than in a lab. Thus, it is important to validate AR for gait-impaired stroke patients in a home setting for accurate clinical predictions. OBJECTIVE: In this study, we sought to evaluate AR performance in a home setting for individuals who had suffered a stroke, by using different sets of training activities. Specifically, we compared AR performance for persons with stroke while varying the origin of training data, based on either population (healthy persons or persons with stoke) or environment (laboratory or home setting). METHODS: Thirty individuals with stroke and fifteen healthy subjects performed a series of mobility-related activities, either in a laboratory or at home, while wearing a smartphone. A custom-built app collected signals from the phone's accelerometer, gyroscope, and barometer sensors, and subjects self-labeled the mobility activities. We trained a random forest AR model using either healthy or stroke activity data. Primary measures of AR performance were (1) the mean recall of activities and (2) the misclassification of stationary and ambulatory activities. RESULTS: A classifier trained on stroke activity data performed better than one trained on healthy activity data, improving average recall from 53% to 75%. The healthy-trained classifier performance declined with gait impairment severity, more often misclassifying ambulatory activities as stationary ones. The classifier trained on in-lab activities had a lower average recall for at-home activities (56%) than for in-lab activities collected on a different day (77%). CONCLUSIONS: Stroke-based training data is needed for high quality AR among gait-impaired individuals with stroke. Additionally, AR systems for home and community monitoring would likely benefit from including at-home activities in the training data.

Sensorimotor recalibration during split-belt walking: task-specific and multisensory?

Motor adaptations not only recalibrate movement execution but also can lead to altered movement perception in multiple sensory domains. Vazquez, Statton, Busgang, and Bastian (J Neurophysiol 114: 3255-3267, 2015) recently showed that split-belt walking affects perception of leg speed during walking, but not perceptions of leg position during standing and walking or perception of contact force during stepping. Considering their findings within the broader scope of sensorimotor recalibration in other tasks, we suggest that sensorimotor recalibrations are task specific and can be multisensory.

Nicotine Enhances the Hypnotic and Hypothermic Effects of Alcohol in the Mouse.

BACKGROUND: Ethanol (EtOH) and nicotine abuse are 2 leading causes of preventable mortality in the world, but little is known about the pharmacological mechanisms mediating co-abuse. Few studies have examined the interaction of the acute effects of EtOH and nicotine. Here, we examine the effects of nicotine administration on the duration of EtOH-induced loss of righting reflex (LORR) and characterize the nature of their pharmacological interactions in C57BL/6J mice. METHODS: We assessed the effects of EtOH and nicotine and the nature of their interaction in the LORR test using isobolographic analysis after acute injection in C57BL/6J male mice. Next, we examined the importance of receptor efficacy using nicotinic partial agonists varenicline and sazetidine. We evaluated the involvement of major nicotinic acetylcholine receptor (nAChR) subtypes using nicotinic antagonist mecamylamine and nicotinic α4- and α7-knockout mice. The selectivity of nicotine's actions on EtOH-induced LORR was examined by testing nicotine's effects on the hypnotic properties of ketamine and pentobarbital. We also assessed the development of tolerance after repeated nicotine exposure. Last, we assessed whether the effects of nicotine on EtOH-induced LORR extend to hypothermia and EtOH intake in the drinking in the dark (DID) paradigm. RESULTS: We found that acute nicotine injection enhances EtOH's hypnotic effects in a synergistic manner and that receptor efficacy plays an important role in this interaction. Furthermore, tolerance developed to the enhancement of EtOH's hypnotic effects by nicotine after repeated exposure of the drug. α4* and α7 nAChRs seem to play an important role in nicotine-EtOH interaction in the LORR test. In addition, the magnitude of EtOH-induced LORR enhancement by nicotine was more pronounced in C57BL/6J than DBA/2J mice. Furthermore, acute nicotine enhanced ketamine and pentobarbital hypnotic effects in the mouse. Finally, nicotine enhanced EtOH-induced hypothermia but decreased EtOH intake in the DID test. CONCLUSIONS: Our results demonstrate that nicotine synergistically enhances EtOH-induced LORR in the mouse.

Rationality in Human Movement.

It long has been appreciated that humans behave irrationally in economic decisions under risk: they fail to objectively consider uncertainty, costs, and rewards and instead exhibit risk-seeking or risk-averse behavior. We hypothesize that poor estimates of motor variability (influenced by motor task) and distorted probability weighting (influenced by relevant emotional processes) contribute to characteristic irrationality in human movement decisions.