Sleep Quality And Prevalence Of Anxiety And Depression In Young Adults With Concussion History

Background: Concussions are a growing public health concern, and emerging possible long-term health risks may negatively impact an individual’s quality of life. With concussions, there can be a diverse multitude of signs and symptoms, but frequent long-term sequelae of concussions including mood disturbances (e.g. anxiety and depression) and sleep disturbances (e.g. difficulties initiating and maintaining sleep) are given less attention. Few clinicians include mood and sleep disturbances in their concussion assessment and management plan, therefore potentially placing individuals at risk for prolonged disturbances beyond recovery. Purpose: To examine the significance of relationships between sleep quality and the prevalence of mood disturbances in healthy young adults with and without a history of concussions. Methods: Two hundred fifty healthy young adults from 4 universities across the country completed a one-time, 15-20-minute anonymous survey administered via Qualtrics. Survey components consisted of demographic information, concussion history, two mental health questionnaires (State-Trait Anxiety Inventory and Beck Depression Inventory) and two sleep quality questionnaires (Pittsburgh Sleep Quality Index and Insomnia Severity Index). Exclusion criteria consisted of having suffered a concussion or traumatic brain injury within the past 9 months, history of cancerous brain tumors, and diagnosed with post-traumatic stress disorder, schizophrenia, or bipolar disorder. Structural equation modeling (SEM) was used as the primary statistical analysis. Results: Data collection is still in progress. Using SEM, we hypothesize that history of concussion may directly impact anxiety and depression, but sleep quality mediates this relationship. Conclusion: This study will provide foundational insight into the relationship of sleep quality, anxiety, and depression in healthy young adults with concussion history. Understanding the relationship between these areas may help guide clinicians to better recognize and manage these prolonged sleep and mood disturbances that may continue well past recovery.https://ir.library.illinoisstate.edu/urs2021knr/1004/thumbnail.jp

On-site and off-Site long-term economic impacts of soil fertility management practices: The case of maize-based cropping systems in Kenya

"This article analyzes the on-site and off-site economic impacts of various sustainable land management (SLM) practices in Kenya. Long-term trial data are used to establish the relationship between SLM practices and maize yield. The analysis of on-site effects focuses on the profitability of maize production at the farm level, while the examined off-site effects include carbon sequestration and siltation from maize farms, which increase the cost of potable water production. The major contribution of this study is the use of long-term experimental data to estimate the impacts of land management practices on crop yield and consider their off-site benefits and costs. The results of this study show that soil and water conservation (SWC) structures reinforced with leguminous plants are more profitable when the plants are used as fodder, as opposed to situations in which only SWC structures are used. SWC structures reinforced with vegetation had lower maintenance costs, whereas those that were not reinforced with leguminous trees were not profitable over the long-term period. These results suggest that complementary and multipurpose SWC practices are more likely to be adopted compared to non-complementary and/or single-purpose practices. Thus, SWC practices should be promoted as a package of complementary technologies. If it is not feasible to promote a mix of complementary enterprises, high value crops are likely to make SLM practices more profitable. In areas where SLM practices are not profitable, promotion of alternative livelihoods is necessary. Adoption of SLM also provides global environmental services worth 10% of the net present value of the SLM practices over the 50-year period addressed in the present study Finally, the results of this analysis suggest that farmers who offer significant environmental services should be compensated for their efforts." from Author's AbstractAgroforestry, Land degradation, Soil erosion, Sustainable land management, Land management, Economic impacts, maize,

Out of Site out of Mind: Quantifying the Long-term Off-site economic Impacts of Land Degradation in Kenya

This study investigated the private and social returns to Sustainable Land Management (SLM) practices with an objective of finding practices that reduce the on-farm and off-farm negative effects of land degradation. The results show that SLM practices have robust profits for farmers raising dairy cows. Farmers without dairy cows realize profits that are sensitive to input and output prices. Adoption of SLM also provides global environmental services whose value is about 10% of the net present value. The results suggest the need to promote SLM practices with multiple uses and consider ways to compensate farmers who offer significant environmental services.Environmental Economics and Policy,

Towards optimal symbolization for time series comparisons

The abundance and value of mining large time series data sets has long been acknowledged. Ubiquitous in fields ranging from astronomy, biology and web science the size and number of these datasets continues to increase, a situation exacerbated by the exponential growth of our digital footprints. The prevalence and potential utility of this data has led to a vast number of time-series data mining techniques, many of which require symbolization of the raw time series as a pre-processing step for which a number of well used, pre-existing approaches from the literature are typically employed. In this work we note that these standard approaches are sub-optimal in (at least) the broad application area of time series comparison leading to unnecessary data corruption and potential performance loss before any real data mining takes place. Addressing this we present a novel quantizer based upon optimization of comparison fidelity and a computationally tractable algorithm for its implementation on big datasets. We demonstrate empirically that our new approach provides a statistically significant reduction in the amount of error introduced by the symbolization process compared to current state-of-the-art. The approach therefore provides a more accurate input for the vast number of data mining techniques in the literature, providing the potential of increased real world performance across a wide range of existing data mining algorithms and applications