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The Convergence of Digital Health Technologies: The Role of Digital Therapeutics in the Future Healthcare System
Published in Oleksandr Sverdlov, Joris van Dam, Digital Therapeutics, 2023
Joris van Dam, Justin Wright, Graham Jones
Geographically isolated populations could also benefit, providing an ever-present means of communication to those in remote locations, be it a patient grappling with seasonal adjustment disorder in a remote village in rural Scandinavia or a cruise ship employee who must deploy for three months at a time. Who knows where the learnings of such studies could be exploited. One obvious example would be in space travel. By many estimates, human-crewed missions to Mars are envisioned within the following decades. With voyage times of up to several months, a digital therapeutic could likely play an enabling role for those involved.
Jobs
Published in Chaitra H. Nagaraja, Measuring Society, 2019
A statistical technique called seasonal adjustment removes the second type of fluctuation so we can focus on fundamental shifts to the labor force. It results in the smoother blue line in Figure 2.1A, which represents the seasonally adjusted labor force participation rate.
The Impact of the COVID-19 Pandemic on Demand for Emergency Ambulances in Victoria, Australia
Published in Prehospital Emergency Care, 2022
Emily Andrew, Ziad Nehme, Michael Stephenson, Tony Walker, Karen Smith
To estimate the effect of COVID-19 and government restrictions on ambulance demand, the weekly volume of Triple Zero (000) calls since January 2018 was modeled using interrupted time series regression. We used negative binomial models with seasonal adjustment. To account for population growth, the natural log of the estimated residential population of Victoria was included as an offset term in all models. We constructed a model for all Triple Zero (000) calls, and then investigated the effect of COVID-19 within patient subgroups, including each MPDS primary complaint category as well as categories of paramedic final assessment. Newey-West standard errors were used to account for autocorrelation of error terms. Results are presented as incident rate ratios (IRR) and 95% confidence intervals (CI).
Seasonality in pain, sleep and mental distress in patients with chronic musculoskeletal pain at latitude 69° N
Published in Chronobiology International, 2020
Karin Abeler, Trond Sand, Oddgeir Friborg, Svein Bergvik
Seasonal variations in light exposure and climate may influence pain conditions directly, as well as indirectly through variations in mood, fatigue, physical activity or sleep disturbance. As seasonal adjustment of multidisciplinary rehabilitation may benefit chronic pain patients, this study examined seasonal variation in pain and in factors that might influence pain. Confinement to primary musculoskeletal pain was preferred to avoid confounding by any seasonality in comorbid underlying diseases. The first objective was to estimate seasonal variation in pain severity and dissemination with the hypothesis that patients with chronic musculoskeletal pain experience increased pain in winter as compared to summer. The second objective was to estimate seasonal variation in the pain-associated conditions of sleep, mental distress, fatigue and physical activity and whether such variations affect seasonal variation in pain. We hypothesized that such seasonality exists and modifies the season–pain relation.
Detection of excessive activities in time series of graphs
Published in Journal of Applied Statistics, 2020
Suchismita Goswami, Edward J. Wegman
Although considerable work has been done to detect clusters of events or anomaly using scan statistics in spatial statistics and image analysis, relatively less attention has been given to detect anomaly in social networks. Priebe et al. [27] first applied temporal scan statistics for Enron email data to detect anomaly in time series of graphs. The full network was partitioned into disjoint subregions or subnetworks over time to overcome the computational complexity associated with a global network and to uncover the interesting features of a node and neighborhood [26]. However, this model normalized the locality statistic twice to eliminate the trend and assumed short-time, near-stationarity for the null model. They did not consider differencing, seasonal adjustment in the univariate time series of scan statistics to make the time series stationary. Furthermore, they assumed that the subgraphs are disjoint. However, the scan statistics with fixed and disjoint scan window may not be appropriate because of the occurrence of window overlaps, which may result in loss of some data on the time axis.