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Interaction with online workbooks was combined with navigation coaching delivered by phone and secure text messaging. In a two-level linear mixed-effects model intention-to-treat analysis, significant between-group differences were found for the Beck Depression Inventory-II score (difference -8.54, P=.01), Quick Inventory of Depressive Symptoms score (difference -4.94, P=.001), Beck Anxiety Inventory score (difference -11.29, P<.001), and Brief Pain Inventory score (difference -1.99, P=.03), while marginal differences were found for the Five Facet Mindfulness Questionnaire-Nonjudging subscale (difference -2.68, P=.05). These results confirm that youth depression can be effectively treated with online CBT-M that can be delivered with less geographic restriction. Clinical Trials.gov NCT03406052; https//www.clinicaltrials.gov/ct2/show/NCT03406052.Clinical Trials.gov NCT03406052; https//www.clinicaltrials.gov/ct2/show/NCT03406052. Chatbots are artificial intelligence-driven programs that interact with people. The applications of this technology include the collection and delivery of information, generation of and responding to inquiries, collection of end user feedback, and the delivery of personalized health and medical information to patients through cellphone- and web-based platforms. However, no chatbots have been developed for patients with lung cancer and their caregivers. This study aimed to develop and evaluate the early feasibility of a chatbot designed to improve the knowledge of symptom management among patients with lung cancer in Japan and their caregivers. We conducted a sequential mixed methods study that included a web-based anonymized questionnaire survey administered to physicians and paramedics from June to July 2019 (phase 1). Two physicians conducted a content analysis of the questionnaire to curate frequently asked questions (FAQs; phase 2). Based on these FAQs, we developed and integrated a chatbot into a s specified in this chatbot to educate patients on how they can manage their symptoms. Further studies are required to improve chatbots in terms of interaction with patients. Bulbar involvement is a term used in amyotrophic lateral sclerosis (ALS) that refers to motor neuron impairment in the corticobulbar area of the brainstem, which produces a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar involvement is voice deterioration characterized by grossly defective articulation; extremely slow, laborious speech; marked hypernasality; and severe harshness. Bulbar involvement requires well-timed and carefully coordinated interventions. Therefore, early detection is crucial to improving the quality of life and lengthening the life expectancy of patients with ALS who present with this dysfunction. Recent research efforts have focused on voice analysis to capture bulbar involvement. The main objective of this paper was (1) to design a methodology for diagnosing bulbar involvement efficiently through the acoustic parameters of uttered vowels in Spanish, and (2) to demonstrate that the performance of the automated diagnosis of bulbar involvement is superior telp in the diagnosis of ALS by multidisciplinary clinical teams, in particular to improve the diagnosis of bulbar involvement. The ongoing changes in population demographics increase the relevance of dignified aging across Europe. Community-based health care (CBHC) organizations are necessary to provide sustainable strategies for organizing care for older persons in need of support. To support the digitalization of these organizations, new business models and suitable web platforms are necessary. This study, which is part of the European Active and Assisted Living (AAL) project called "ICareCoops?", aimed to explore concepts, approaches, and workflows of CBHC organizations to achieve a comprehensive understanding of extant services offered and relevant requirements to support these services with information and computer technology (ICT) solutions. A qualitative study with six focus groups (FGs) with 40 participants was conducted in Switzerland and Slovenia to identify potential stakeholders' needs and requirements for the user-centered development of a web platform. Data were collected from three different stakeholder groups (1) demonstrate a high potential to address users' needs. Therefore, the web platform offers an essential tool for innovative health care models in the future. Searching for care services, contacting care providers, and communicating with care providers was preferred via personal contact and seemed to be the key element for user acceptance and for the successful implementation of a web platform like "ICareCoops?" to support CBHC organizations. Preventive primary care programs that aim to reduce morbidity and mortality from lifestyle-related diseases are often affected by low-to-moderate participation rates. Improving participation rates is essential for clinical effectiveness and cost-effectiveness. In 2016-2017, we conducted a pilot study (TOF pilot1) for a preventive primary care intervention (TOF is the Danish abbreviation for "Early Detection and Prevention"). Among 8814 invited patients, 3545 (40.22%) consented to participate, with the highest participation rates among women and patients with higher income, education, and employment. The aim of this study was to evaluate the effects of a revised invitation strategy on invitation comprehensibility, the overall participation rate, and participant demography. The new strategy specifically targeted men and patients of low educational attainment. This study was embedded in the second TOF pilot study (TOF pilot2, initiated in October 2018) that tested an adjusted intervention. The revised invi reduce social inequality in health. ClinicalTrials.gov NCT03913585; https//clinicaltrials.gov/ct2/show/NCT03913585.ClinicalTrials.gov NCT03913585; https//clinicaltrials.gov/ct2/show/NCT03913585. Increasing screen time exposure from digital devices like smartphones has shown a variety of mixed associations with cognition, behavior, and well-being in adults and children but little is known about its associations with symptomatology in individuals with serious mental illness. To determine the range of associations between screen time and symptoms of individuals with mental illness, we utilized a method called specification curve analysis. In this observational study, we recruited smartphone-owning adults (≥18 years old) with schizophrenia and healthy controls. We installed 2 research-source smartphone apps, mindLAMP and Beiwe, to collect survey results, cognitive test results, and screen time metrics over a period of 3 months. Surveys were scheduled for twice a week, but participants were instructed to take the surveys naturally as much or as little as they wanted. Screen time was collected continuously in the background. https://www.selleckchem.com/products/me-401.html A total of 140 participants was recruited from the outpatient clinic population as well as through general public advertising.