Translating Research to Practice for Children With Autism Spectrum Disorder: Part 2: Behavior Management in Home and Health Care Settings

Introduction Managing home and health care for children with autism spectrum disorder can be challenging because of the range of symptoms and behaviors exhibited. Method This article presents an overview of the emerging science related to the methods to foster family self-management of common concerns regarding activities of daily living and behaviors, as well as for the health care provider in primary and acute health care settings. Results Recommendations are provided to enhance the overall delivery of services, including understanding and managing a child\u27s challenging behaviors, and supporting family management of common activities of daily living and behaviors. Discussion Health care providers\u27 knowledge of evidence-based recommendations for providing care, supporting family self-management of common concerns, and referral heighten the likelihood of better outcomes for children with autism spectrum disorder

Breathing Signature as Vitality Score Index Created by Exercises of Qigong: Implications of Artificial Intelligence Tools Used in Traditional Chinese Medicine.

Rising concerns about the short- and long-term detrimental consequences of administration of conventional pharmacopeia are fueling the search for alternative, complementary, personalized, and comprehensive approaches to human healthcare. Qigong, a form of Traditional Chinese Medicine, represents a viable alternative approach. Here, we started with the practical, philosophical, and psychological background of Ki (in Japanese) or Qi (in Chinese) and their relationship to Qigong theory and clinical application. Noting the drawbacks of the current state of Qigong clinic, herein we propose that to manage the unique aspects of the Eastern 'non-linearity' and 'holistic' approach, it needs to be integrated with the Western "linearity" "one-direction" approach. This is done through developing the concepts of "Qigong breathing signatures," which can define our life breathing patterns associated with diseases using machine learning technology. We predict that this can be achieved by establishing an artificial intelligence (AI)-Medicine training camp of databases, which will integrate Qigong-like breathing patterns with different pathologies unique to individuals. Such an integrated connection will allow the AI-Medicine algorithm to identify breathing patterns and guide medical intervention. This unique view of potentially connecting Eastern Medicine and Western Technology can further add a novel insight to our current understanding of both Western and Eastern medicine, thereby establishing a vitality score index (VSI) that can predict the outcomes of lifestyle behaviors and medical conditions

An Investigation of the Perceptions of Community Residents and Volunteer Researchers for the Community Health of the Clarke Square Neighborhood

The study explored the similarities and differences in community residents’ perceptions and volunteer researchers’ perceptions of community health indicators in the Clarke Square Neighborhood. The study sought to identify noteworthy differences between community residents’ perceptions and volunteer researchers’ perceptions, indicating the importance of including community members in community health research. In the study, community residents in the Clarke Square Neighborhood conducted a survey that had previously been done by volunteer researchers from the Urban Ecology Center and the Medical College of Wisconsin regarding community health indicators in the Clarke Square Neighborhood. The study also included a focus group, which discussed the perceptions of community residents and volunteer researchers regarding community health in the Clarke Square Neighborhood and the role that communities play in research. The study found several noteworthy differences in the perceptions of community residents and volunteer researchers regarding community health in the Clarke Square Neighborhood

Research Advances: January 2014

The VA has a comprehensive research agenda to help the newest generation of Veterans -- those returning from operations Enduring Freedom, Iraqi Freedom, and New Dawn. In addition to exploring new treatments for traumatic brain injury and other complex blast-related injuries, VA researchers are examining ways to improve the delivery of health care services for these Veterans and promote their reintegration back into their families, communities, and workplaces.This publication reviews recent advances in research about Veterans' health and well-being

Theatre is a valid add-on therapeutic intervention for emotional rehabilitation of parkinson's disease patients

Conventional medical treatments of Parkinson's disease (PD) are effective on motor disturbances but may have little impact on nonmotor symptoms, especially psychiatric ones. Thus, even when motor symptomatology improves, patients might experience deterioration in their quality of life. We have shown that 3 years of active theatre is a valid complementary intervention for PD as it significantly improves the well-being of patients in comparison to patients undergoing conventional physiotherapy. Our aim was to replicate these findings while improving the efficacy of the treatment. We ran a single-blinded pilot study lasting 15 months on 24 subjects with moderate idiopathic PD. 12 were assigned to a theatre program in which patients underwent "emotional" training. The other 12 underwent group physiotherapy. Patients were evaluated at the beginning and at the end of their treatments, using a battery of eight clinical and five neuropsychological scales. We found that the emotional theatre training improved the emotional well-being of patients, whereas physiotherapy did not. Interestingly, neither of the groups showed improvements in either motor symptoms or cognitive abilities tested by the neuropsychological battery. We confirmed that theatre therapy might be helpful in improving emotional well-being in PD

The effect of strategies of personal resilience on depression recovery in an Australian cohort : a mixed methods study

Strategies of personal resilience enable successful adaptation in adversity. Among patients experiencing depression symptoms, we explored which personal resilience strategies they find most helpful, and tested the hypothesis that use of these strategies improves depression recovery. We used interview and survey data from the Diagnosis, Management and Outcomes of Depression in Primary Care 2005 cohort of patients experiencing depression symptoms in Victoria, Australia. 564 participants answered a computer assisted telephone interview question at 12 months follow-up, about what they found most helpful for their depression, stress or worries. Depressive disorder and severity were measured at annual follow-up using the Composite International Diagnostic Interview and the PHQ-9 self-rating questionnaire. Using interview responses we categorised participants as users or not of strategies of personal resilience, specifically, drawing primarily on expanding their own inner resources or pre-existing relationships: 316 (56%) were categorised as primarily users of personal resilience strategies. Of these, 193 (61%) reported expanding inner resources, 79 (25%) drawing on relationships, and 44 (14%) reported both. There was no association between drawing on relationships and depression outcome. There was evidence supporting an association between expanding inner resources and depression outcome: 25% of users having major depressive disorder one year later compared to 38% of non-users (adjusted OR 0.59, CI 0.36-0.97). This is the first study to show improved outcome for depression for those who identify as most helpful the use of personal resilience strategies. The difference in outcome is important as expanding inner resources includes a range of low intensity, yet commonly available strategies

A Mixed-Method Investigation into Therapeutic Yoga as an Adjunctive Treatment for People Recovering from Substance Use Disorders

© 2020, The Author(s). Mind Body Connect (MBC) is a charity which uses therapeutic yoga as a vehicle of change for marginalized populations. Alongside MBC, Sheffield Hallam University’s SHU Strength researchers carried out this study aiming to: (1) Gauge the impact of therapeutic yoga classes upon the mood state of people with a Substance Use Disorder (SUD) and (2) Explore the perceived benefits of therapeutic yoga class participation. An adapted shortened Profile of Mood States (POMS) was completed before and after each yoga class. A comparison of means with paired sample T-Test and Cohen’s D was then carried out. Participants who attended 6+ classes were interviewed. Findings were then converged. Before and after measurements of anger, sadness, tiredness, worry, confusion, energy and relaxation were taken, Classes were held at SHU for service users from a Phoenix Future’s (PF) rehabilitation centre. A single yoga class significantly relaxed participants and reduced negative mood states. Interview data covered a range of perceived benefits including the use of yogic down-regulation techniques as daily coping strategies. The MBC yoga programme appears beneficial as an adjunctive therapy for PF residents. Future SHU Strength research shall focus on the mid-long-term exercise habits of the recovery community and the impact of the MBC yoga programme upon the early recovery period of detoxification

PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data

Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson's disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson's disease.Comment: AAAI Conference on Artificial Intelligence 201

Automated voice pathology discrimination from audio recordings benefits from phonetic analysis of continuous speech

In this paper we evaluate the hypothesis that automated methods for diagnosis of voice disorders from speech recordings would benefit from contextual information found in continuous speech. Rather than basing a diagnosis on how disorders affect the average acoustic properties of the speech signal, the idea is to exploit the possibility that different disorders will cause different acoustic changes within different phonetic contexts. Any differences in the pattern of effects across contexts would then provide additional information for discrimination of pathologies. We evaluate this approach using two complementary studies: the first uses a short phrase which is automatically annotated using a phonetic transcription, the second uses a long reading passage which is automatically annotated from text. The first study uses a single sentence recorded from 597 speakers in the Saarbrucken Voice Database to discriminate structural from neurogenic disorders. The results show that discrimination performance for these broad pathology classes improves from 59% to 67% unweighted average recall when classifiers are trained for each phone-label and the results fused. Although the phonetic contexts improved discrimination, the overall sensitivity and specificity of the method seems insufficient for clinical application. We hypothesise that this is because of the limited contexts in the speech audio and the heterogeneous nature of the disorders. In the second study we address these issues by processing recordings of a long reading passage obtained from clinical recordings of 60 speakers with either Spasmodic Dysphonia or Vocal fold Paralysis. We show that discrimination performance increases from 80% to 87% unweighted average recall if classifiers are trained for each phone-labelled region and predictions fused. We also show that the sensitivity and specificity of a diagnostic test with this performance is similar to other diagnostic procedures in clinical use. In conclusion, the studies confirm that the exploitation of contextual differences in the way disorders affect speech improves automated diagnostic performance, and that automated methods for phonetic annotation of reading passages are robust enough to extract useful diagnostic information