Moselaursen3746

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. A total of 140 participants was recruited from the outpatient clinic populatent and dependent variables selected for analysis.

The associations between screen time and mental health in patients with schizophrenia are heterogenous when examined with methods that reduce analytical bias. The heterogeneity in associations suggests that complex and personalized potential effects must be understood in the greater context of an individual. This analysis of longitudinally collected screen time data shows potential for future research that could benefit from high resolution metrics on smartphone use.

The associations between screen time and mental health in patients with schizophrenia are heterogenous when examined with methods that reduce analytical bias. The heterogeneity in associations suggests that complex and personalized potential effects must be understood in the greater context of an individual. This analysis of longitudinally collected screen time data shows potential for future research that could benefit from high resolution metrics on smartphone use.

Incomplete suicidality coding in administrative claims data is a known obstacle for observational studies. With most of the negative outcomes missing from the data, it is challenging to assess the evidence on treatment strategies for the prevention of self-harm in bipolar disorder (BD), including pharmacotherapy and psychotherapy. There are conflicting data from studies on the drug-dependent risk of self-harm, and there is major uncertainty regarding the preventive effect of monotherapy and drug combinations.

The aim of this study was to compare all commonly used BD pharmacotherapies, as well as psychotherapy for the risk of self-harm, in a large population of commercially insured individuals, using self-harm imputation to overcome the known limitations of this outcome being underrecorded within US electronic health care records.

The IBM MarketScan administrative claims database was used to compare self-harm risk in patients with BD following 65 drug regimens and drug-free periods. Probable but uncoded roate, risperidone, aripiprazole, SNRI, selective serotonin reuptake inhibitor (SSRI), "no drug," bupropion, and bupropion + SSRI (HRs 0.28-0.74). Psychotherapy alone (without medication) had a lower self-harm risk than no treatment (HR 0.56, 95% CI 0.52-0.60; P=8.76×10

). The sensitivity analysis showed that the direction of drug-outcome associations did not change as a function of the self-harm probability threshold.

Our data support evidence on the effectiveness of antidepressants, MSAs, and psychotherapy for self-harm prevention in BD.

ClinicalTrials.gov NCT02893371; https//clinicaltrials.gov/ct2/show/NCT02893371.

ClinicalTrials.gov NCT02893371; https//clinicaltrials.gov/ct2/show/NCT02893371.

More than 50% of all patients with breast cancer experience fatigue symptoms during and after their treatment course. Recent evidence has shown that fatigue is partly driven by cognitive biases such as the self-as-fatigued identity bias, which may be corrected with computer-based cognitive bias modification (CBM) techniques.

The aim of this study was to design a CBM-training app by adopting a cocreation approach.

Semistructured interviews were conducted with 7 health care professionals, 3 patients with breast cancer, and 2 patient advocates. The aim of the interviews was to collect input for the design of the CBM training, taking the values and preferences of the stakeholders into account, and to determine the timing and implementation of the training in the treatment course.

Overall, the interviews showed that the concept of CBM was accepted among all stakeholders. Important requirements were revealed such as the training needs to be simple and undemanding, yet engaging and persuasive. Simufilam clinical trial Based on the results, an eHealth app IVY (Implicit VitalitY) was created. The findings from the interviews suggested that IVY should be offered early in the breast cancer treatment course and should be carefully aligned with clinical treatment.

The findings of this study show that using CBM as a preventive approach to target cancer-related fatigue is an innovative technique, and this approach was embraced by breast cancer stakeholders. Our study suggests that CBM training has several benefits such as being easy to use and potentially increasing perceived self-control in patients.

The findings of this study show that using CBM as a preventive approach to target cancer-related fatigue is an innovative technique, and this approach was embraced by breast cancer stakeholders. Our study suggests that CBM training has several benefits such as being easy to use and potentially increasing perceived self-control in patients.

Successful management of gestational diabetes mellitus (GDM) reduces the risk of morbidity in women and newborns. A woman's blood glucose readings and risk factors are used by clinical staff to make decisions regarding the initiation of pharmacological treatment in women with GDM. Mobile health (mHealth) solutions allow the real-time follow-up of women with GDM and allow timely treatment and management. Machine learning offers the opportunity to quickly analyze large quantities of data to automatically flag women at risk of requiring pharmacological treatment.

The aim of this study is to assess whether data collected through an mHealth system can be analyzed to automatically evaluate the switch to pharmacological treatment from diet-based management of GDM.

We collected data from 3029 patients to design a machine learning model that can identify when a woman with GDM needs to switch to medications (insulin or metformin) by analyzing the data related to blood glucose and other risk factors.

Through the analysis of 411,785 blood glucose readings, we designed a machine learning model that can predict the timing of initiation of pharmacological treatment.