Background: The COVID-19 pandemic was marked by rapidly evolving and inconsistent public health messaging, contributing to confusion regarding recommended preventive behaviors. Knowledge, attitudes, and practices (KAP) and perceived risk frameworks offer a structured approach to examine how education, personal beliefs, and contextual factors influence health behaviors during public health emergencies. Vulnerable populations, such as patients with multiple sclerosis (MS), experience heightened risk perception compared with the general population, which may further shape behavioral responses. Objective: This study aimed to examine COVID-19–related KAP and perceived risk among patients with MS, health care providers, and laypeople during the first 6 months of the pandemic. The aim of mixed methods was to explore quantitative factors associated with KAP and perceived risk and to qualitatively describe participants’ perceptions and emotional responses to the pandemic. Methods: A descriptive, cross-sectional, partially mixed methods explanatory sequential design was used. Participants were recruited using convenience sampling and completed an online demographic questionnaire and a COVID-19 KAP instrument that included perceived risk items. Quantitative data were analyzed using descriptive statistics and inferential analyses to examine group differences and associations between perceived risk and preventive behaviors. Chi-square testing was applied to compare perceived risk across groups, and correlational analyses were used to examine the relationships between perceived risk and behavioral practices. Qualitative comments provided by participants were analyzed using thematic analysis to further contextualize quantitative findings and to explore perceived risk experiences. Results: A total of 148 participants were included, comprising 43 (29%) individuals with MS, 50 (33.8%) health care providers, and 55 (37.2%) laypeople. Overall, 90% (n=133) of participants demonstrated basic knowledge of COVID-19 transmission and prevention. Attitudes toward public health guidance and self-reported preventive behaviors varied across groups. Lay participants most frequently reported a moderate perceived risk of COVID-19 infection, whereas participants with MS and health care providers more commonly reported high perceived risk (²=12.65, =.049). Neither immunosuppressive treatment status nor vaccine hesitancy significantly predicted perceived risk. However, higher perceived risk was significantly associated with greater avoidance of crowded and public places. Qualitative analysis yielded 5 interrelated themes describing participants’ perceived risk experiences: uncertainty related to evolving scientific information; anxiety regarding personal and family safety; fear of infection and long-term consequences; vulnerability, particularly among individuals with chronic illness and frontline exposure; and accountability toward protecting others through adherence to preventive measures. These themes provided contextual insight into the emotional and cognitive processes underlying reported attitudes and behaviors. Conclusions: Knowledge of COVID-19 is associated with favorable attitudes and engagement in preventive practices across populations. Differences in perceived risk highlight the importance of tailoring risk communication and educational strategies to specific populations. KAP-focused educational interventions that explicitly address uncertainty, emotional responses, and behavioral translation may strengthen preparedness and promote sustained protective behaviors during future public health emergencies. Trial Registration: ClinicalTrials.gov NCT07021716; https://clinicaltrials.gov/ct2/show/NCT07021716
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Effects of an Eye-Tracking Digital Serious Game on Cognitive Function in Mild Cognitive Impairment: Pilot Intervention Study
<strong>Background:</strong> Cognitive decline in aging populations underscores the need for early interventions in mild cognitive impairment (MCI), where pharmacological treatments show limited benefit. Eye-movement metrics serve as sensitive markers of cognitive deficits in MCI, and digital programs integrating these tasks offer scalable, data-driven training approaches. <strong>Objective:</strong> This study aimed to evaluate the effectiveness of a digital cognitive training program incorporating eye-movement tasks in individuals with MCI, and to determine whether eye-movement indicators can serve as objective markers of cognitive improvement. <strong>Methods:</strong> A total of 12 participants aged 60-85 years with MCI (Korean version of the Montreal Cognitive Assessment [K-MoCA] score of ≤22) completed baseline and postintervention assessments using the K-MoCA and Mini-Mental State Examination-Korean version (MMSE-K). Longitudinal changes in visuospatial attention and oculomotor performance were examined using Spearman correlations across sessions, and pre-post comparisons of eye-tracking metrics were conducted to assess training-related improvements. <strong>Results:</strong> Cognitive scores improved significantly, with K-MoCA increasing by 1.5 points (from mean 20.3, SD 1.1 to mean 21.8, SD 1.7; <i>P</i>=<i>.</i>004; Cohen <i>d</i>=1.38) and MMSE-K by 1.3 points (from mean 21.9, SD 2.0 to mean 23.2, SD 2.2; <i>P</i>=<i>.</i>002; Cohen <i>d</i>=1.29). Fixation duration decreased (<i>r</i>=0.248; <i>P</i>=<i>.</i>003), and saccade velocity increased (<i>r</i>=0.258; <i>P</i>=<i>.</i>002), indicating enhanced visual processing efficiency and faster attentional shifts, whereas fixation count and saccade amplitude showed no consistent changes. In addition, saccade duration decreased by 21.72 ms, and saccade velocity increased by 114.54 °/s. <strong>Conclusions:</strong> Digital cognitive training yielded measurable gains in visuospatial attention and oculomotor efficiency in MCI, with optimized fixation and saccade patterns indicating enhanced attentional control and information processing. These findings support eye-movement metrics as sensitive indicators of cognitive change and highlight digital interventions as scalable, noninvasive tools for cognitive support in aging populations.
Mobile App–Based Smoking Cessation in Hispanic or Latino Adults: Culturally Tailored Spanish-Language Formative App Development Study
Background: Despite the notable proliferation of smoking cessation mobile apps, to date, no validated, Spanish-language, culturally tailored mobile intervention exists for Spanish speakers in the United States. Objective: The aim of this study was to conduct formative research to inform the adaptation of an evidence-based smoking cessation intervention developed for Spanish-speaking Hispanic and Latino individuals from a printed format into a mobile app. Methods: Guided by a user-centered approach and in collaboration with product design industry experts, wireframes were developed to present the app’s layout and functionality. Focus groups were conducted over Zoom (Zoom Communications) with Spanish-speaking individuals who currently smoke to assess their previous mobile app experience, attitudes toward mobile apps, and feedback on app architecture and design. Two independent reviewers (RB in collaboration with another member from the qualitative core) trained in qualitative methods coded the focus group data using a thematic analysis approach and identified emerging themes. Results: The app wireframes included 4 navigation buttons on the home screen to organize and deliver evidence-based intervention content—Home (), Learn (), My Coach (), and Profile (). Different wireframe designs were generated in distinct color palettes. Data saturation was reached after three focus groups. Participants were 54% (7/13) women, had a mean age of 56 (SD 14.9) years, 39% (5/13) had an education ≤high school, and 31% (4/13) were married or cohabitating. All participants smoked daily, a mean of 14 (SD 7.8) cigarettes per day, for 32 (SD 16.9) years, and 54% (7/13) smoked ≤30 minutes of waking. Participants reported using social media, news, shopping, and gaming apps, but few used mobile health apps. Salient barriers for app use included worries regarding privacy breaches and fears about misinformation. Desired features included community-building elements, personalization, reward badges, knowledge checks, and audiovisual presentation of content within the app. Participants disliked having a countdown to quit date, preferring an “I quit” button to initiate monitoring progress. They also viewed sharing progress with support networks as a source of unwanted pressure, although a few saw it as motivational. Overall, participants liked the app design and indicated willingness to use it. Conclusions: This formative research provides critical insights into preferences related to the development of culturally tailored mobile smoking cessation interventions for Spanish-speaking individuals. Key findings highlighted enthusiasm for a smoking cessation app and the importance of including features that foster social connection and allow for personalization.
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Primary Care Physicians’ Interactions With a Novel Web-Based Active Learning Tool (The Community Fracture Capture Learning Hub): Qualitative Analysis
Background: Osteoporosis poses a significant global health burden and is responsible for over 8.9 million fragility fractures annually. Despite evidence-based guidelines and treatment, a substantial care gap persists, with only a low percentage of fracture patients receiving guideline-concordant care. Primary care physicians (PCPs) are pivotal in community-based fracture prevention but face challenges in translating knowledge into practice. While hospital-based fracture liaison services are effective, their reach is limited, necessitating scalable alternatives. Virtual communities of practice and web-based learning tools offer promising avenues for PCP professional education; however, their application in osteoporosis management remains underexplored. The Community Fracture Capture (CFC) Learning Hub was developed as an interactive, case-based platform to address these gaps by enhancing PCPs’ knowledge, confidence, and engagement in osteoporosis care. Objective: The study aimed to conduct a qualitative evaluation of PCPs’ interactions with the CFC Learning Hub, focusing on barriers and facilitators of the online learning tool and exploring PCP perceptions of the program. Methods: A qualitative analysis was performed using data from 55 PCPs across four 6-week cycles of the CFC Learning Hub (May 2022-October 2023). Data sources included discussion forum comments and responses to open-ended questions in end-of-cycle evaluations. Relational content analysis was used, with 2 researchers independently coding data using semantic and latent approaches. Themes were identified through iterative discussions and validated against existing literature. Results: Four themes emerged from PCP interactions: (1) user experience–guided platform design, where participants emphasized intuitive navigation, minimized fragmented sections, and clarity of interface as critical for engagement; (2) learning-supportive course structure, highlighting the importance of explicit links between case studies and foundational knowledge, weekly summaries, and quizzes aligned with content; (3) learners’ different styles and preferences, with diverging needs for synchronous vs asynchronous learning, didactic sessions, and peer-to-peer interactions; and (4) program content, where participants requested expanded topics and postprogram refreshers. Conclusions: The CFC Learning Hub demonstrated efficacy as a specialist- and peer-to-peer–supported online learning model for PCPs, addressing osteoporosis care gaps through user-centered design, adaptable content delivery, and collaborative moderation. Key successes included resolving usability issues iteratively and accommodating diverse learning preferences. These findings underscore the potential of the Hub to enhance primary care professional education and fracture prevention. The study advocates for broader adoption of the platform to bridge osteoporosis care disparities.
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Automated Safety Testing and Reporting Application for Conversational Safety Monitoring of Generative AI Tools for Mental Health: Development and Validation Study
<strong>Background:</strong> Artificial intelligence (AI)–based conversational tools are rapidly expanding within mental health care as a means of increasing access and scalability. At the same time, these systems introduce distinct safety risks arising from both user disclosures (eg, self-harm ideation) and inappropriate or inadequate AI responses. <strong>Objective:</strong> This study aimed to develop and evaluate the Automated Safety Testing and Reporting Application (ASTRA), an external system intended to identify clinically relevant risk behaviors across entire AI-mediated mental health conversations. <strong>Methods:</strong> ASTRA was tested on a dataset of 100 synthetic therapeutic conversations written by licensed clinicians to reflect risk behaviors and harmful responses between users and AI tools. Conversations varied in length and included both subtle and overt risk behavior examples across 8 predefined categories. Human coder consensus ratings served as the reference standard. ASTRA’s classifications were evaluated across 2 prompt iterations using standard diagnostic performance metrics and agreement statistics. <strong>Results:</strong> ASTRA demonstrated consistently high concordance with expert human ratings across all categories. Accuracy exceeded 0.90 for all risk behavior categories examined, with specificity uniformly high and sensitivity varying by category (range 0.55-1.00). Agreement beyond chance was substantial to almost perfect between ASTRA and human raters (κ=0.65-1.00). Detection of user self-harm indicators was particularly accurate, even in conversations where risk was expressed subtly. <strong>Conclusions:</strong> In this initial validation study, ASTRA reliably identified multiple forms of mental health–related risk behaviors at the conversation level. These findings support the feasibility of independent safety monitoring systems as a complement to AI tools used in mental health contexts and underscore the need for further evaluation using larger and real-world datasets.
Digitally Delivered Cognitive Behavioral Interventions for Alcohol and Other Drug Use: Meta-Analysis Across Consumption and Psychosocial Outcomes
<strong>Background:</strong> Cognitive behaviorally based interventions have broad appeal and potential for impact when treating adult alcohol and other drug use. Digitally delivered cognitive behaviorally based interventions (dCBIs) may offer this impact with the benefit of increased accessibility. Although prior reviews have indicated the benefits of dCBIs on substance use outcomes, the extension to psychosocial functioning outcomes is unknown. <strong>Objective:</strong> This meta-analysis provides an overview of dCBI effects across a range of functional end points. <strong>Methods:</strong> A literature search was conducted through October 2024. All primary and secondary reports of clinical trials of dCBI were obtained, and all available study end points were eligible for meta-analysis. Descriptive data were extracted and categorized into 1 of 13 different outcome types (eg, abstinence, quantity, cognitive, and quality of life) and into 2 broader outcome classes (ie, consumption and psychosocial). Robust variance estimation was used to conduct hypothesis tests on random effects pooled estimates with outcome class and comparison type as the primary subgroup variables of interest. <strong>Results:</strong> The study sample included 65 randomized trials (<i>K</i>=110 publications; 753 effect sizes) of dCBI for adult alcohol and other drug use. With respect to efficacy, dCBI as a stand-alone treatment in contrast to a minimal treatment control showed positive and statistically significant effects for consumption (<i>g</i>=0.27; <i>P<</i>.001; <i>I</i><sup>2</sup>=85.1%; <i>k</i>=31; <i>k<sub>es</sub></i>=134) and psychosocial (<i>g</i>=0.16; <i>P</i>=.008; <i>I</i><sup>2</sup>=75.2%; <i>k</i>=16; <i>k<sub>es</sub></i>=60) outcomes. As an addition to usual care, efficacy was demonstrated for consumption (<i>g</i>=0.23; <i>P<</i>.001; <i>I</i><sup>2</sup>=9.8%; <i>k</i>=20; <i>k<sub>es</sub></i>=65), but not psychosocial functioning. Efficacy compared to another digital or in-person intervention or cognitive behaviorally based intervention delivered by a therapist was not observed. Within the dCBI condition, large effect sizes were observed for both outcome classes (ie, 60%-80% of participants showed improvement relative to baseline), and effect size magnitude and statistical heterogeneity varied by the type of outcome examined. <strong>Conclusions:</strong> These results show a benefit for dCBI as a stand-alone therapy and an addition to usual care. Importantly, stand-alone effects were observed for both consumption and some psychosocial outcomes. This study is the first to offer a comprehensive look at dCBI intervention effects across a range of functional end points.
STAT+: Virginia governor vetoes legislation to create an advisory panel to lower the cost of prescription drugs
Virginia Gov. Abigail Spanberger has vetoed closely watched legislation to create an advisory panel to lower prescription drug costs, a setback to attempts by lawmakers across the United States to address the rising cost of medicines.
Unlike affordability boards in other states, the Virginia panel would have used Medicare as a benchmark. Rather than start from scratch to identify drugs considered expensive, each year the panel would have targeted the same drugs chosen by Medicare for price negotiations. The board would have also set upper payment limits to create a ceiling on what would be paid.
By doing so, Virginia would have leapfrogged plans by other states that are at varying stages of establishing affordability boards. Of the other nine states that have boards, none is eyeing all of the same drugs chosen annually by Medicare and only four have the authority to set upper payment limits.
STAT+: This spine surgery usually costs $1,400. Under No Surprises Act arbitration? $34,000
When health insurers contract with providers, they agree on prices for all kinds of procedures. For a lumbar laminectomy, a common spine surgery for ailments like herniated discs or arthritis, the median price is $1,400.
Out-of-network providers, those that don’t contract with health insurers, are getting 24 times that amount for the same surgery at the median — nearly $34,000 — through the No Surprises Act’s arbitration process. Some are even getting north of $100,000.
The lumbar laminectomy is just one example of the extraordinarily high awards being doled out under the flawed system created by the 2020 law. The law has successfully protected patients from unexpected bills, but it’s also been a major boon for providers. They’re not only securing massive sums when they win, but they’re also prevailing in over 80% of disputes.
How AI helped treat a newborn’s ultra rare disease. ‘It was almost like a light switch.’
In the first, tenuous weeks of her life, Jorie Kraus and her parents faced her possible death repeatedly. Muscles throughout her tiny body simply didn’t work properly. Her heart. Her legs. Her larynx. Even the involuntary action of breathing was labored, and constantly faltering.
In those panicked days, through a haze of terrible news and incomprehensible instructions, something incredible happened: A long-shot attempt to discover the root cause of her problems identified a widely available, yet previously unknown, treatment.
Roundtables: Inside the Musk v. Altman Trial
Listen to the session or watch below
Elon Musk lost his suit against OpenAI, in which he alleged CEO Sam Altman and President Greg Brockman had deceived him over the company’s non-profit status.
Watch as AI reporter and attorney Michelle Kim, who covered the trial for MIT Technology Review, joins in conversation with editor in chief Mat Honan to go behind the scenes of the trial and the implications for the AI race.
Speakers: Mat Honan, Editor in Chief, and Michelle Kim, AI Reporter
Recorded on May 19, 2026
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