Background: Asthma is one of the most common chronic diseases in childhood. Regular follow-up of physiological parameters in the home setting, in relation to asthma symptoms, can provide complementary quantitative insights into the dynamics of the asthma status. Despite considerable interest in asthma home-monitoring in children, there is a paucity of scientific evidence, especially on multi-parameter monitoring approaches. Therefore, the aim of this study is to investigate whether asthma control can be accurately assessed in the home situation by combining parameters from respiratory physiology sensors. Methods: Sixty asthmatic and thirty non-asthmatic children were enrolled in the observational WEARCON-study. Asthma control was assessed according to GINA guidelines by the paediatrician. All children were also evaluated during a 2-week home-monitoring period with wearable devices; a physical activity tracker, a handheld spirometer, smart inhalers, and an ambulatory electrocardiography device to monitor heart and respiratory rate. Multiple logistic regression analysis was used to determine which diagnostic measures were associated with asthma control. Results: 24 of the 27 uncontrolled asthmatic children and 29 of the 32 controlled asthmatic children could be accurately identified with this model. The final model showed that a larger variation in pre-exercise lung function (OR = 1.34 95%-CI 1.07-1.68), an earlier wake-up-time (OR = 1.05 95%-CI 1.01-1.10), more reliever use (OR = 1.11 95%-CI 1.03-1.19) and a longer respiratory rate recovery time (OR = 1.12 95%-CI 1.05-1.20) were significant contributors to the probability of having uncontrolled asthma. Conclusions: Home-monitoring of physiological parameters correlates with paediatrician assessed asthma control. The constructed multivariate model identifies 88.9% of all uncontrolled asthmatic children, indicating a high potential for monitoring of asthma control. This may allow healthcare professionals to assess asthma control at home.
Background Cueing strategies can alleviate freezing of gait (FOG) in people with Parkinson’s disease (PD). We evaluated tactile cueing delivered via vibrating socks, which has the benefit of not being noticeable to bystanders. Objective To evaluate the effect of tactile cueing compared to auditory cueing on FOG. Methods Thirty-one persons with PD with FOG performed gait tasks during both ON and OFF state. The effect of open loop and closed loop tactile cueing, as delivered by vibrating socks, was compared to an active control group (auditory cueing) and to a baseline condition (uncued gait). These four conditions were balanced between subjects. Gait tasks were videotaped and annotated for FOG by two experienced raters. Motion data were collected to analyze spatiotemporal gait parameters. Responders were defined as manifesting a relative reduction of > 10% in the percent time frozen compared to uncued gait. Results The average percent time frozen during uncued gait was 11.2% in ON and 21.5% in OFF state. None of the three tested cueing modalities affected the percentage of time frozen in either the ON (p = 0.20) or OFF state (p = 0.12). The number of FOG episodes and spatiotemporal gait parameters were also not affected. We found that 22 out of 31 subjects responded to cueing, the response to the three types of cueing was highly individual. Conclusions Cueing did not improve FOG at the group level; however, tactile as well as auditory cueing improved FOG in many individuals. This highlights the need for a personalized approach when using cueing to treat FOG.
Freezing of gait, a disabling episodic symptom, is usually difficult to assess as the exact begin and endpoint of an episode is not easy to specify. This hampers scientific and clinical progress. The current golden standard is video annotation by two independent raters. However, the comparison of the two ratings gives also rise to non-overlapping, grey areas and there is currently no consensus on how these areas should be handled. We here propose a theoretical guideline and open-source practical tool, FOGtool, for dealing with these grey areas based on two parameters, the tolerance and correction parameter. Furthermore, we recommend the use of positive agreement, negative agreement, and prevalence index to report interrater agreement instead of the commonly used intraclass correlation coefficient or Cohen's kappa. This paper aims to contribute to the standardization of freezing of gait assessment, thereby improving data sharing procedures and replicability of study results.
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