Sitting and standing performance in a total population of children with cerebral palsy: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Knowledge of sitting and standing performance in a total population of children with cerebral palsy (CP) is of interest for health care planning and for prediction of future ability in the individual child. In 1994, a register and a health care programme for children with CP in southern Sweden was initiated. In the programme information on how the child usually sits, stands, stands up and sits down, together with use of support or assistive devices, is recorded annually.</p> <p>Methods</p> <p>A cross-sectional study was performed, analysing the most recent report of all children with CP born 1990-2005 and living in southern Sweden during 2008. All 562 children (326 boys, 236 girls) aged 3-18 years were included in the study. The degree of independence, use of support or assistive devices to sit, stand, stand up and sit down was analysed in relation to the Gross Motor Function Classification System (GMFCS), CP subtype and age.</p> <p>Result</p> <p>A majority of the children used standard chairs (57%), could stand independently (62%) and could stand up (62%) and sit down (63%) without external support. Adaptive seating was used by 42%, external support to stand was used by 31%, to stand up by 19%, and to sit down by 18%. The use of adaptive seating and assistive devices increased with GMFCS levels (p < 0.001) and there was a difference between CP subtypes (p < 0.001). The use of support was more frequent in preschool children aged 3-6 (p < 0.001).</p> <p>Conclusion</p> <p>About 60% of children with CP, aged 3-18, use standard chairs, stand, stand up, and sit down without external support. Adding those using adaptive seating and external support, 99% of the children could sit, 96% could stand and 81% could stand up from a sitting position and 81% could sit down from a standing position. The GMFCS classification system is a good predictor of sitting and standing performance.</p

Predicting developmental changes in internalizing symptoms: Examining the interplay between parenting and neuroendocrine stress reactivity

In this study, we examined whether parenting and HPA‐axis reactivity during middle childhood predicted increases in internalizing symptoms during the transition to adolescence, and whether HPA‐axis reactivity mediated the impact of parenting on internalizing symptoms. The study included 65 children (35 boys) who were assessed at age 5, 7, and 11. Parenting behaviors were assessed via parent report at age 5 and 11. The child's HPA‐axis reactivity was measured at age 7 via a stress task. Internalizing symptoms were measured via teacher reports at age 5 and 11. High maternal warmth at age 5 predicted lower internalizing symptoms at age 11. Also, high reported maternal warmth and induction predicted lower HPA‐axis reactivity. Additionally, greater HPA‐axis reactivity at age 7 was associated with greater increases in internalizing symptoms from age 5 to 11. Finally, the association between age 5 maternal warmth and age 11 internalizing symptoms was partially mediated by lower cortisol in response to the stress task. Thus, parenting behaviors in early development may influence the physiological stress response system and therefore buffer the development of internalizing symptoms during preadolescence when risk for disorder onset is high. © 2013 Wiley Periodicals, Inc. Dev Psychobiol 56: 908–923, 2014.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107358/1/dev21166.pd

Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡

While modern low-power microcontrollers are a cornerstone of wearable physiological sensors, their limited on-chip storage typically makes peripheral storage devices a requirement for long-term physiological sensing&mdash;significantly increasing both size and power consumption. Here, a wearable biosensor system capable of long-term recording of physiological signals using a single, 64 kB microcontroller to minimize sensor size and improve energy performance is described. Electrodermal (EDA) signals were sampled and compressed using a multiresolution wavelet transformation to achieve long-term storage within the limited memory of a 16-bit microcontroller. The distortion of the compressed signal and errors in extracting common EDA features is evaluated across 253 independent EDA signals acquired from human volunteers. At a compression ratio (CR) of 23.3&times;, the root mean square error (RMSErr) is below 0.016 &mu; S and the percent root-mean-square difference (PRD) is below 1%. Tonic EDA features are preserved at a CR = 23.3&times; while phasic EDA features are more prone to reconstruction errors at CRs &gt; 8.8&times;. This compression method is shown to be competitive with other compressive sensing-based approaches for EDA measurement while enabling on-board access to raw EDA data and efficient signal reconstructions. The system and compression method provided improves the functionality of low-resource microcontrollers by limiting the need for external memory devices and wireless connectivity to advance the miniaturization of wearable biosensors for mobile applications

An Ultra-Low Resource System for Electrodermal Activity Monitoring

Wearable biosensors and mobile healthcare (mHealth) technologies are revolutionizing modern healthcare delivery by providing access to pragmatic, medical-grade services that are scalable outside of the hospital setting. Electrodermal activity (EDA) is a physiological signal of particular importance within the mHealth community because it is a useful marker for the physiological arousal of the sympathetic nervous system used in studies of depression, anxiety disorders, stress management, and many more. EDA refers to electrical variations in skin conductance and capacitance occurring at the surface of the skin due to changes in sweat secretion. Modern EDA biosensors often require significant analog and digital resources to acquire and record high-quality EDA signals due to the wide range and variability of skin conductivity across populations. There are significant challenges in maintaining a balance between high-performance sensing capabilities of a biosensor and its ability to be small in size, unobtrusive, and long-lasting. The work within this thesis addresses the research question of how low-resource digital design can be used to improve the size, power efficiency, and utility of wearable EDA sensors while maintaining high-quality physiological sensing capabilities. An ultra-low resource system for EDA measurement is presented that implements a quasi-digital EDA sensor topology for measuring both the conductive and capacitive components of the EDA signal and requires no analog-to-digital converters or in-phase and quadrature demodulation. Additionally, we apply on-board compression and storage of the EDA signal within a 16-bit microcontroller to improve sensor size and power efficiency by removing the external data storage and transmission requirements for long-term EDA monitoring. The accuracy, precision, dynamic range, and power efficiency of the developed system is characterized and the devices are evaluated in a pilot study

Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ^‡

While modern low-power microcontrollers are a cornerstone of wearable physiological sensors, their limited on-chip storage typically makes peripheral storage devices a requirement for long-term physiological sensing&#8212;significantly increasing both size and power consumption. Here, a wearable biosensor system capable of long-term recording of physiological signals using a single, 64 kB microcontroller to minimize sensor size and improve energy performance is described. Electrodermal (EDA) signals were sampled and compressed using a multiresolution wavelet transformation to achieve long-term storage within the limited memory of a 16-bit microcontroller. The distortion of the compressed signal and errors in extracting common EDA features is evaluated across 253 independent EDA signals acquired from human volunteers. At a compression ratio (CR) of 23.3&#215;, the root mean square error (RMSErr) is below 0.016 &#956; S and the percent root-mean-square difference (PRD) is below 1%. Tonic EDA features are preserved at a CR = 23.3&#215; while phasic EDA features are more prone to reconstruction errors at CRs &gt; 8.8&#215;. This compression method is shown to be competitive with other compressive sensing-based approaches for EDA measurement while enabling on-board access to raw EDA data and efficient signal reconstructions. The system and compression method provided improves the functionality of low-resource microcontrollers by limiting the need for external memory devices and wireless connectivity to advance the miniaturization of wearable biosensors for mobile applications

Continuous Detection of Physiological Stress with Commodity Hardware

Timely detection of an individual’s stress level has the potential to improve stress management, thereby reducing the risk of adverse health consequences that may arise due to mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical-grade sensors to measure physiological signals; they are often bulky, custom made, and expensive, hence limiting their adoption by researchers and the general public. In this article, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, nonclinical sensors to capture physiological signals and make inferences about the wearer’s stress level based on that data. We describe a system involving a popular off-the-shelf heart rate monitor, the Polar H7; we evaluated our system with 26 participants in both a controlled lab setting with three well-validated stress-inducing stimuli and in free-living field conditions. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an F1-score of up to 0.87 in the lab and 0.66 in the field, on par with clinical-grade sensors