ABSTRACT
(1) Background: Artificial intelligence (AI) has flourished in recent years. More specifically, generative AI has had broad applications in many disciplines. While mental illness is on the rise, AI has proven valuable in aiding the diagnosis and treatment of mental disorders. However, there is little to no research about precisely how much interest there is in AI technology. (2) Methods: We performed a Google Trends search for "AI and mental health" and compared relative search volume (RSV) indices of "AI", "AI and Depression", and "AI and anxiety". This time series study employed Box-Jenkins time series modeling to forecast long-term interest through the end of 2024. (3) Results: Within the United States, AI interest steadily increased throughout 2023, with some anomalies due to media reporting. Through predictive models, we found that this trend is predicted to increase 114% through the end of the year 2024, with public interest in AI applications being on the rise. (4) Conclusions: According to our study, we found that the awareness of AI has drastically increased throughout 2023, especially in mental health. This demonstrates increasing public awareness of mental health and AI, making advocacy and education about AI technology of paramount importance.
Subject(s)
Artificial Intelligence , Mental Health , United States , Humans , Depression/epidemiology , Mental Disorders/epidemiology , Mental Disorders/therapy , Anxiety/epidemiologyABSTRACT
BACKGROUND: Mobile health technology's impact on cardiovascular risk factor control is not fully understood. This study evaluates the association between interaction with a mobile health application and change in cardiovascular risk factors. METHODS AND RESULTS: Participants with hypertension with or without dyslipidemia enrolled in a workplace-deployed mobile health application-based cardiovascular risk self-management program between January 2018 and December 2022. Retrospective evaluation explored the influence of application engagement on change in blood pressure (BP), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and weight. Multiple regression analyses examined the influence of guideline-based, nonpharmacological lifestyle-based digital coaching on outcomes adjusting for confounders. Of 102 475 participants, 49.1% were women. Median age was 53 (interquartile range, 43-61) years, BP was 134 (interquartile range, 124-144)/84 (interquartile range, 78-91) mm Hg, TC was 183 (interquartile range, 155-212) mg/dL, LDL-C was 106 (82-131) mg/dL, and body mass index was 30 (26-35) kg/m2. At 2 years, participants with baseline systolic BP ≥140 mm Hg reduced systolic BP by 18.6 (SEM, 0.3) mm Hg. At follow up, participants with baseline TC ≥240 mg/dL reduced TC by 65.7 (SEM, 4.6) mg/dL, participants with baseline LDL-C≥160 mg/dL reduced LDL-C by 66.6 (SEM, 6.2) mg/dL, and participants with baseline body mass index ≥30 kg/m2 lost 12.0 (SEM, 0.3) pounds, or 5.1% of body weight. Interaction with digital coaching was associated with greater reduction in all outcomes. CONCLUSIONS: A mobile health application-based cardiovascular risk self-management program was associated with favorable reductions in BP, TC, LDL-C, and weight, highlighting the potential use of this technology in comprehensive cardiovascular risk factor control.
Subject(s)
Cardiovascular Diseases , Heart Disease Risk Factors , Self-Management , Telemedicine , Humans , Female , Male , Middle Aged , Self-Management/methods , Adult , Retrospective Studies , Cardiovascular Diseases/prevention & control , Cardiovascular Diseases/epidemiology , Cardiovascular Diseases/etiology , Cardiovascular Diseases/blood , Dyslipidemias/blood , Dyslipidemias/diagnosis , Dyslipidemias/therapy , Dyslipidemias/epidemiology , Mobile Applications , Hypertension/physiopathology , Hypertension/therapy , Blood Pressure/physiology , Cholesterol, LDL/blood , Risk Reduction BehaviorABSTRACT
The COVID-19 pandemic exposed and exacerbated longstanding inefficiencies and deficiencies in chronic disease management and treatment in the United States, such as a fragmented healthcare experience and system, narrowly focused services, limited resources beyond office visits, expensive yet low quality care, and poor access to comprehensive prevention and non-pharmacological resources. It is feared that the addition of COVID-19 survivors to the pool of chronic disease patients will burden an already precarious healthcare system struggling to meet the needs of chronic disease patients. Digital health and telemedicine solutions, which exploded during the pandemic, may address many inefficiencies and deficiencies in chronic disease management, such as increasing access to care. However, these solutions are not panaceas as they are replete with several limitations, such as low uptake, poor engagement, and low long-term use. To fully optimize digital health and telemedicine solutions, we argue for the gamification of digital health and telemedicine solutions through a pantheoretical framework-one that uses personalized, contextualized, and behavioral science algorithms, data, evidence, and theories to ground treatments.
Subject(s)
COVID-19 , Pandemics , Chronic Disease , Delivery of Health Care , Humans , SARS-CoV-2 , United States/epidemiologyABSTRACT
Decision aids are evidenced-based tools designed to increase patient understanding of medical options and possible outcomes, facilitate conversation between patients and clinicians, and improve patient engagement. Decision aids have been used for shared decision making (SDM) interventions outside of the ED setting for more than a decade. Their use in the ED has only recently begun to be studied. This article provides background on this topic and the conclusions of the 2016 Academic Emergency Medicine consensus conference SDM in practice work group regarding "Shared Decision Making in the Emergency Department: Development of a Policy-Relevant, Patient-Centered Research Agenda." The goal was to determine a prioritized research agenda for the development and testing of SDM interventions for use in emergency care that was most important to patients, clinicians, caregivers, and other key stakeholders. Using the nominal group technique, the consensus working group proposed prioritized research questions in six key domains: 1) content (i.e., clinical scenario or decision area), 2) level of evidence available, 3) tool design strategies, 4) risk communication, 5) stakeholders, and 6) outcomes.
Subject(s)
Decision Making , Decision Support Techniques , Emergency Medicine/organization & administration , Emergency Service, Hospital/organization & administration , Patient Participation , Communication , Humans , Patient-Centered Care , RiskABSTRACT
PURPOSE: The purpose of this clinical pilot project was to evaluate the effectiveness of a 12 week lifestyle change program targeted to patients with chronic disease. DATA SOURCES: Data were collected weekly from participants using individual and group feedback and body composition analysis. CONCLUSIONS: The Game of Health was well received by patients and was effective in modifying behaviors to achieve a healthier lifestyle and to improve body composition. Primary care providers need to consider how to make lifestyle change programs available to their patients to complement clinical interventions.