The wrist is not the brain: Estimation of sleep by clinical and consumer wearable actigraphy devices is impacted by multiple patient‐ and device‐specific factors

Clinical actigraphy devices provide adequate estimates of some sleep measures across large groups. In practice, providers are asked to apply clinical or consumer wearable data to individual patient assessments. Inter‐individual variability in device performance will impact such patient‐specific interpretation. We assessed two devices, clinical and consumer, to determine the magnitude and predictors of this individual‐level variability. One hundred and two patients (55 [53.9%] female; 56.4 [±16.3] years old) undergoing polysomnography wore Jawbone UP3 and/or Actiwatch2. Device total sleep time, sleep efficiency, wake after sleep onset and sleep latency were compared with polysomnography. Demographics, sleep architecture and clinical measures were compared to device performance. Actiwatch overestimated total sleep time by 27.2 min (95% confidence limits [CL], 138.3 min over to 84.0 under), overestimated sleep efficiency by 6.8% (95% CL, 34.1% over to 20.5% under), overestimated sleep onset latency by 2.6 min (95% CL, 63.3 over to 58.2 under) and underestimated wake after sleep onset by 50.7 min (95% CL, 162.5 under to 61.2 over). Jawbone overestimated total sleep time by 59.1 min (95% CL, 208.6 min over to 90.5 under) and overestimated sleep efficiency by 14.9% (95% CL, 52.6% over to 22.7% under). In multivariate models, age, sleep onset latency, wake after sleep onset, % N1 and apnea–hypopnea index explained only some of the variance in device performance. Gender also affected performance. Actiwatch and Jawbone mis‐estimate sleep measures with very wide confidence limits and accuracy varies with multiple patient‐level characteristics. Given these large individual inaccuracies, data from these devices must be applied only with extreme caution in clinical practice.     

Temperature Measurement by Sublimation Rate as a Process Analytical Technology Tool in Lyophilization

Product temperature (T_b) and drying time constitute critical material attributes and process parameters in the lyophilization process and especially during the primary drying stage. In the study, we performed a temperature measurement by the sublimation rate (TMbySR) to monitor the T_b value and determine the end point of primary drying. First, the water vapor transfer resistance coefficient through the main pipe from the chamber to the condenser (C_r) was estimated via the water sublimation test. The use of C_r value made it possible to obtain the time course of T_b from the measurement of pressure at the drying chamber and at the condenser. Second, a Flomoxef sodium bulk solution was lyophilized by using the TMbySR system. The outcome was satisfactory when compared with that obtained via conventional sensors. The same was applicable for the determination of the end point of primary drying. A laboratory-scale application of the TMbySR system was evidenced via the experiment using 220-, 440-, and 660-vial scales of lyophilization. The outcome was not dependent on the loading amount. Thus, the results confirmed that the TMbySR system is a promising tool in laboratory scale.     

Cancer Drug Dosing in Chronic Kidney Disease and Dialysis

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Meeting the challenges of measuring outcomes of home care programs: The Australian Community Outcomes Measurement (ACCOM) tool

ABSTRACT

Measuring the impact of care delivered at home for frail older people is a complex task given many confounding variables that may impact on the ability of service providers to identify the direct impact of their programs on their clients’ well-being and quality of life. The recent publication of the 2018 Wellness and Reablement Report Outcomes indicated that organizations lack formal processes to measure the impact of their programs on service users. There are therefore limited data exits on measuring outcomes and the performance of the ACCOM tool in the real world. Knowledge of a strong causal relationship between services provided and outcomes enables confidence in assuming the care provided was largely responsible for the outcome achieved. This paper will reflect on the experiences of one service provider in Brisbane, in implementing the Australian Community Care Outcomes Measurement (ACCOM) tool to measure and demonstrate the impact of their programs.     

Ambulatory activity classification with dendogram-based support vector machine: Application in lower-limb active exoskeleton

Ambulatory activity classification is an active area of research for controlling and monitoring state initiation, termination, and transition in mobility assistive devices such as lower-limb exoskeletons. State transition of lower-limb exoskeletons reported thus far are achieved mostly through the use of manual switches or state machine-based logic. In this paper, we propose a postural activity classifier using a ‘dendogram-based support vector machine’ (DSVM) which can be used to control a lower-limb exoskeleton.A pressure sensor-based wearable insole and two six-axis inertial measurement units (IMU) have been used for recognising two static and seven dynamic postural activities: sit, stand, and sit-to-stand, stand-to-sit, level walk, fast walk, slope walk, stair ascent and stair descent. Most of the ambulatory activities are periodic in nature and have unique patterns of response. The proposed classification algorithm involves the recognition of activity patterns on the basis of the periodic shape of trajectories. Polynomial coefficients extracted from the hip angle trajectory and the centre-of-pressure (CoP) trajectory during an activity cycle are used as features to classify dynamic activities.The novelty of this paper lies in finding suitable instrumentation, developing post-processing techniques, and selecting shape-based features for ambulatory activity classification. The proposed activity classifier is used to identify the activity states of a lower-limb exoskeleton. The DSVM classifier algorithm achieved an overall classification accuracy of 95.2%.