Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process.

Objectives: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

Considerations for the Design of Informed Consent in Digital Health Research: Participant Perspectives.

The research team, prospective participants, and written materials all influence the success of the informed consent process. As digital health research becomes more prevalent, new challenges for successful informed consent are introduced. This exploratory research utilized a human centered design process in which 19 people were enrolled to participate in one of four online focus-groups.

Remote data collection of infant activity and sleep patterns via wearable sensors in the HEALthy Brain and Child Development Study (HBCD).

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Wearable and remote sensing technologies have advanced data collection outside of laboratory settings to enable exploring, in more detail, the associations of early experiences with brain development and social and health outcomes. In the HBCD Study, the Novel Technology/Wearable Sensors Working Group (WG-NTW) identified two primary data types to be collected: infant activity (by measuring leg movements) and sleep (by measuring heart rate and leg movements).

Achieving Inclusive Healthcare through Integrating Education and Research with AI and Personalized Curricula.

Precision medicine promises significant health benefits but faces challenges such as the need for complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) like GPT-4 and Claude 3 highlights the importance of making complex data accessible to non-specialists.

Evaluating Users' Experiences of a Child Multimodal Wearable Device: Mixed Methods Approach.

Background: Wearable devices permit the continuous, unobtrusive collection of data from children in their natural environments and can transform our understanding of child development. Although the use of wearable devices has begun to emerge in research involving children, few studies have considered families' experiences and perspectives of participating in research of this kind.

Objective: Through a mixed methods approach, we assessed parents' and children's experiences of using a new wearable device in the home environment.

Digital exposure notification tools: A global landscape analysis.

Background: As the COVID-19 pandemic continues, digital exposure notification systems are increasingly used to support traditional contact tracing and other preventive strategies. Likewise, a plethora of COVID-19 mobile applications have emerged. Objective: To characterize the global landscape of pandemic related mobile applications, including digital exposure notification and contact tracing tools.

Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting.

Purpose: Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These "Exposure Notification (EN)" systems present new opportunities to support public health interventions. To date, there have been attempts to model the impact of such systems, yet no reports have explored the value of real-time system data for predictive epidemiological modeling.

"Click and mortar" opportunities for digitization and consumerism in trials.

Background: Digitization (using novel digital tools and strategies) and consumerism (taking a consumer-oriented approach) are increasingly commonplace in clinical trials, but the implications of these changes are not well described.

Methods: We assembled a group of trial experts from academia, industry, non-profit, and government to discuss implications of this changing trial landscape and provide guidance.

Results: Digitization and consumerism can increase the volume and diversity of trial participants and expedite recruitment.

Evaluating Mobile Apps Targeting Older Adults: Descriptive Study.

Background: Smartphone use has increased dramatically and, in parallel, a market for mobile apps, including health apps, has emerged. The business model of targeted mobile app advertisements allows for the collection of personal and potentially sensitive information, often without user knowledge. Older adults comprise a rapidly growing demographic that is potentially vulnerable to exploitation by those accessing data collected via these apps.

The Independent Walking for Brain Health Intervention for Older Adults: Protocol for a Pilot Randomized Controlled Trial.

Background: Extensive research suggests that physical activity (PA) is important for brain and cognitive health and may help to delay or prevent Alzheimer's disease and related dementias. Most PA interventions designed to improve brain health in older adults have been conducted in laboratory, gym, or group settings that require extensive resources and travel to the study site or group sessions. Research is needed to develop novel interventions that leverage mobile health (mHealth) technologies to help older adults increase their engagement in PA in free-living environments, reducing participant burden and increasing generalizability of research findings.

Defining Key Performance Indicators for the California COVID-19 Exposure Notification System (CA Notify).

Objectives: Toward common methods for system monitoring and evaluation, we proposed a key performance indicator framework and discussed lessons learned while implementing a statewide exposure notification (EN) system in California during the COVID-19 epidemic.

Materials And Methods: California deployed the Google Apple Exposure Notification framework, branded CA Notify, on December 10, 2020, to supplement traditional COVID-19 contact tracing programs. For system evaluation, we defined 6 key performance indicators: adoption, retention, sharing of unique codes, identification of potential contacts, behavior change, and impact.

Perceptions of Pharmacy Graduate Students Toward Research Ethics Education: A Cross-Sectional Study from a Developing Country.

Despite the potential value of graduate-level research ethics training, most Middle East countries, including Jordan, do not routinely offer formal research ethics training. In students enrolled in Jordanian master's level graduate program in pharmacy, the current study assessed: 1- differences in pre- and post-enrollment exposure to research ethics core themes, 2- whether this exposure was through a formal course or in an informal setting, and 3- student attitudes towards research ethics education and the need for integrating a dedicated research ethics course into pharmacy graduate programs. A 12-item on-line survey was developed by the authors and disseminated to a convenience sample of current and former master-level pharmacy students in Jordan.

Optimizing Ethics Engagement in Research: Learning from the Ethical Complexities of Studying Opioid Use in Pregnancy.

Research on opioid use in pregnancy is critically important to understand how the opioid epidemic has affected a generation of children, but also raises significant ethical and legal challenges. Embedded ethicists can help to fill the gaps in ethics oversight for such research, but further guidance is needed to help strike the balance between integration and independence.

Social Determinants of Health Data Availability for Patients with Eye Conditions.

Purpose: To quantify and characterize social determinants of health (SDoH) data coverage using single-center electronic health records (EHRs) and the National Institutes of Health All of Us research program.

Design: Retrospective cohort study from June 2014 through June 2021.

Participants: Adults 18 years of age or older with a diagnosis of diabetic retinopathy, glaucoma, cataracts, or age-related macular degeneration.

Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets.

Background: With the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated analysis of the drawing process has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its applicability across populations.

Lessons Learned: Beta-Testing the Digital Health Checklist for Researchers Prompts a Call to Action by Behavioral Scientists.

Digital technologies offer unique opportunities for health research. For example, Twitter posts can support public health surveillance to identify outbreaks (eg, influenza and COVID-19), and a wearable fitness tracker can provide real-time data collection to assess the effectiveness of a behavior change intervention. With these opportunities, it is necessary to consider the potential risks and benefits to research participants when using digital tools or strategies.

Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing.

Social isolation and loneliness (SI/L) are growing problems with serious health implications for older adults, especially in light of the COVID-19 pandemic. We examined transcripts from semi-structured interviews with 97 older adults (mean age 83 years) to identify linguistic features of SI/L. Natural Language Processing (NLP) methods were used to identify relevant interview segments (responses to specific questions), extract the type and number of social contacts and linguistic features such as sentiment, parts-of-speech, and syntactic complexity.

Applying a Digital Health Checklist and Readability Tools to Improve Informed Consent for Digital Health Research.

As research involving human participants increasingly occurs with the aid of digital tools (e.g., mobile apps, wearable and remote pervasive sensors), the consent content and delivery process is changing.

Learning From Older Adults to Promote Independent Physical Activity Using Mobile Health (mHealth).

Healthy aging is critically important for several reasons, including economic impact and quality of life. As the population of older adults rapidly increases, identifying acceptable ways to promote healthy aging is a priority. Technologies that can facilitate health promotion and risk reduction behaviors may be a solution, but only if these mobile health (mHealth) tools can be used by the older adult population.