Health information influences consumer decision making to seek, select, and utilize services. Online searching for mental health information is increasingly common, especially by adolescents and parents. We examined historical trends and factors that may influence population-level patterns in information seeking for attention-deficit/hyperactivity disorder (ADHD). We extracted Google Trends data from January 2004 to February 2020. Keywords included "ADHD," "ADHD treatment," "ADHD medication," and "ADHD therapy." We examined trends (systematic change over time) and seasonality (repeating pattern of change) via time-series analyses and graphics. We also used interrupted time-series analyses to examine the impact of celebrity and pharmaceutical events. Queries of "ADHD medication" increase, while queries for "ADHD therapy" remain relatively low despite a positive linear trend. Searches for "ADHD treatment" displayed a downward trend in more recent years. Analyses on seasonality revealed that holiday breaks coincided with a decrease in search interest, while post-break periods illustrated a rise, and the ADHD Awareness Month (October) coincided with a rise of public interest in all four search terms. Celebrity effects were more prominent in earlier years; the "Own It" pharmaceutical campaign may have increased ADHD awareness and the specificity of searches for "ADHD medication." The anonymous, accessible, and low-cost nature of seeking information online makes search engines like Google important sources of mental health information. Changing search patterns in response to seasonal, advocacy, and media events highlight internet-based opportunities for raising awareness and disseminating empirically supported information.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s10488-021-01168-w | DOI Listing |
Neuromodulation
January 2025
Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Objectives: Biphasic sinusoidal repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation treatment that has been approved by the US Food and Drug Administration for treatment-resistant depression (TRD). Recent advances suggest that standard rTMS may be improved by altering the pulse shape; however, there is a paucity of research investigating pulse shape, owing primarily to the technologic limitations of currently available devices. This pilot study examined the feasibility, tolerability, and preliminary efficacy of biphasic and monophasic rectangular rTMS for TRD.
View Article and Find Full Text PDFThe current study aims to determine how the interactions between practice (distributed/focused) and mental capacity (high/low) in the cloud-computing environment (CCE) affect the development of reproductive health skills and cognitive absorption. The study employed an experimental design, and it included a categorical variable for mental capacity (low/high) and an independent variable with two types of activities (distributed/focused). The research sample consisted of 240 students from the College of Science and College of Applied Medical Sciences at the University of Hail's.
View Article and Find Full Text PDFViruses
November 2024
Department of Toxicology, Drug Industry, Management and Legislation, Faculty of Pharmacy, "Victor Babeş" University of Medicine and Pharmacy, 2nd Eftimie Murgu Sq., 300041 Timişoara, Romania.
The COVID-19 outbreak, caused by the SARS-CoV-2 virus, was linked to significant neurological and psychiatric manifestations. This review examines the physiopathological mechanisms underlying these neuropsychiatric outcomes and discusses current management strategies. Primarily a respiratory disease, COVID-19 frequently leads to neurological issues, including cephalalgia and migraines, loss of sensory perception, cerebrovascular accidents, and neurological impairment such as encephalopathy.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Instituto de Estudios de Género, Universidad Carlos III de Madrid, Calle Madrid, 126, 28903 Getafe, Spain.
Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!