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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., Materials and Methods: We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle that consolidates these themes into a more comprehensive lifecycle. We then considered the potential benefits and harms of AI through this lifecycle to identify ethical questions that can arise at each step and to identify where conflicts and errors could arise in ethical analysis. We illustrated the approach in 3 case studies that highlight how different ethical dilemmas arise at different points in the lifecycle., Results Discussion and Conclusion: Through case studies, we show how a systematic lifecycle-informed approach to the ethical analysis of AI enables mapping of the effects of AI onto different steps to guide deliberations on benefits and harms. The lifecycle-informed approach has broad applicability to different stakeholders and can facilitate communication on ethical issues for patients, healthcare professionals, research participants, and other stakeholders., Competing Interests: The authors have no competing interests to report., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Biomedical research and clinical practice are in the midst of a transition toward significantly increased use of artificial intelligence (AI) and machine learning (ML) methods. These advances promise to enable qualitatively deeper insight into complex challenges formerly beyond the reach of analytic methods and human intuition while placing increased demands on ethical and explainable artificial intelligence (XAI), given the opaque nature of many deep learning methods. The U.S. National Institutes of Health (NIH) has initiated a significant research and development program, Bridge2AI, aimed at producing new "flagship" datasets designed to support AI/ML analysis of complex biomedical challenges, elucidate best practices, develop tools and standards in AI/ML data science, and disseminate these datasets, tools, and methods broadly to the biomedical community. An essential set of concepts to be developed and disseminated in this program along with the data and tools produced are criteria for AI-readiness of data, including critical considerations for XAI and ethical, legal, and social implications (ELSI) of AI technologies. NIH Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group members prepared the present article to present methods for assessing the AI-readiness of biomedical data and the data standards perspectives and criteria we have developed throughout this program. While the field is rapidly evolving, these criteria are foundational for scientific rigor and the ethical design and application of biomedical AI methods.

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. Participants discussed their experiences with informed consent, preferences for receiving study information and ideas about alternative consent approaches. Data were analyzed using qualitative methods. Six major themes and sixteen sub-themes were identified that included study information that prospective participants would like to receive, preferences for accessing information and a desire to connect with research team members. Specific to digital health, participants expressed a need to understand how the technologies worked and how the volume of granular personal information would be collected, stored, and shared., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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). These wearable technologies allow for remote collection in the natural environment. This paper illustrates the collection of such data via wearable technologies and describes the decision-making framework, which led to the currently deployed study design, data collection protocol, and derivatives, which will be made publicly available. Moreover, considerations regarding actual and potential challenges to adoption and use, data management, privacy, and participant burden were examined. Lastly, the present limitations in the field of wearable sensor data collection and analysis will be discussed in terms of extant validation studies, the difficulties in comparing performance across different devices, and the impact of evolving hardware/software/firmware., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

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. The Stanford Data Ocean (SDO) strives to mitigate these challenges through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning in precision medicine. SDO provides AI tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible for users from diverse educational backgrounds. By extending engagement and cutting-edge research capabilities globally, SDO particularly benefits economically disadvantaged and historically marginalized communities, fostering interdisciplinary biomedical research and bridging the gap between education and practical application in the biomedical field.

Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health., (©Meelim Kim, Kevin Patrick, Camille Nebeker, Job Godino, Spencer Stein, Predrag Klasnja, Olga Perski, Clare Viglione, Aaron Coleman, Eric Hekler. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.03.2024.)

The ethical, legal, and social implications (ELSIs) of digital health are important when researchers and practitioners are using technology to collect, process, or store personal health data. Evidence underscores a strong need for digital health ELSI training, yet little is known about the specific ELSI topic areas that researchers and practitioners would most benefit from learning. To identify ELSI educational needs, a needs assessment survey was administered to the members of the Society of Behavioral Medicine (SBM). We sought to identify areas of ELSI proficiency and training need, and also evaluate interest and expertise in ELSI topics by career level and prior ELSI training history. The 14-item survey distributed to SBM members utilized the Digital Health Checklist tool (see recode.health/tools) and included items drawn from the four-domain framework: data management, access and usability, privacy and risk to benefit assessment. Respondents (N = 66) were majority faculty (74.2%) from psychology or public health. Only 39.4% reported receiving "formal" ELSI training. ELSI topics of greatest interest included practices that supported participant engagement, and dissemination and implementation of digital tools beyond the research setting. Respondents were least experienced in managing "bystander" data, having discussions about ELSIs, and reviewing terms of service agreements and privacy policies with participants and patients. There is opportunity for formalized ELSI training across career levels. Findings serve as an evidence base for continuous and ongoing evaluation of ELSI training needs to support scientists in conducting ethical and impactful digital health research., (© Society of Behavioral Medicine 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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. The wearable device was designed specifically for use with infants and young children, and it integrates audio, electrocardiogram, and motion sensors., Methods: In study 1, semistructured phone interviews were conducted with 42 parents of children aged 1 month to 9.5 years who completed 2 day-long recordings using the device, which the children wore on a specially designed shirt. In study 2, a total of 110 parents of children aged 2 months to 5.5 years responded to a questionnaire assessing their experience of completing 3 day-long device recordings in the home. Guided by the Digital Health Checklist, we assessed parental responses from both studies in relation to the following three key domains: (1) access and usability, (2) privacy, and (3) risks and benefits., Results: In study 1, most parents viewed the device as easy to use and safe and remote visits as convenient. Parents' views on privacy related to the audio recordings were more varied. The use of machine learning algorithms (vs human annotators) in the analysis of the audio data, the ability to stop recordings at any time, and the view that the recordings reflected ordinary family life were some reasons cited by parents who expressed minimal, if any, privacy concerns. Varied risks and benefits were also reported, including perceived child comfort or discomfort, the need to adjust routines to accommodate the study, the understanding gained from the study procedures, and the parent's and child's enjoyment of study participation. In study 2, parents' ratings on 5 close-ended items yielded a similar pattern of findings. Compared with a "neutral" rating, parents were significantly more likely to agree that (1) device instructions were helpful and clear (t 109 =-45.98; P<.001), (2) they felt comfortable putting the device on their child (t 109 =-22.22; P<.001), and (3) they felt their child was safe while wearing the device (t 109 =-34.48; P<.001). They were also less likely to worry about the audio recordings gathered by the device (t 108 =6.14; P<.001), whereas parents' rating of the burden of the study procedures did not differ significantly from a "neutral" rating (t 109 =-0.16; P=.87)., Conclusions: On the basis of parents' feedback, several concrete changes can be implemented to improve this new wearable platform and, ultimately, parents' and children's experiences of using child wearable devices in the home setting., (©Nancy L McElwain, Meghan C Fisher, Camille Nebeker, Jordan M Bodway, Bashima Islam, Mark Hasegawa-Johnson. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 08.02.2024.)

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., Data Sources and Methods: The following queries were entered into the Google search engine: "(*country name* COVID app) OR (COVID app *country name*) OR (COVID app *country name*+) OR (*country name*+ COVID app)". The App Store, Google Play, and official government websites were then accessed to collect descriptive data for each application. Descriptive data were qualified and quantified using standard methods. COVID-19 Exposure Notification Systems (ENS) and non-Exposure Notification products were categorized and summarized to provide a global landscape review., Results: Our search resulted in a global count of 224 COVID-19 mobile applications, in 127 countries. Of these 224 apps, 128 supported exposure notification, with 75 employing the Google Apple Exposure Notification (GAEN) application programming interface (API). Of the 75 apps using the GAEN API, 15 apps were developed using Exposure Notification Express, a GAEN turnkey solution. COVID-19 applications that did not include exposure notifications (n = 96) focused on COVID-19 Self-Assessment (35·4%), COVID-19 Statistics and Information (32·3%), and COVID-19 Health Advice (29·2%)., Conclusions: The digital response to COVID-19 generated diverse and novel solutions to support non-pharmacologic public health interventions. More research is needed to evaluate the extent to which these services and strategies were useful in reducing viral transmission., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Nebeker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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. However, downstream bottlenecks, challenges with retention, and serious issues with equity, ethics, and security can result. A "click and mortar" approach, combining approaches from novel and traditional trials with the thoughtful use of technology, may optimally balance opportunities and challenges facing many trials., Conclusion: We offer expert guidance and three "click and mortar" approaches to digital, consumer-oriented trials. More guidance and research are needed to navigate the associated opportunities and challenges., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Rachel L. Randell is supported by the National Institute of General Medical Sciences and the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers T32GM086330 and T32HD104576. RLR's spouse has financial relationships with Biogen and Merck & Co, Inc. Lesley Curtis received contract support from Janssen and Vir Biotechnology and consulting fees from Regeneron. Adrian F. Hernandez received contract support from Pfizer Inc. and Merck and consulting fees from Merck. Tom Denwood is employed by Population Health Partners LLP. Camille Nebeker has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Jeremy Sugarman receives support from NIH grants. He receives consulting fees as part of an Ethics Advisory panel as well as travel reimbursement from Merck KGaA and IQVIA. Dr. Sugarman participates on a Data Safety Monitoring Board for Merck and a Clinical Advisory Panel for Aspen Neurosciences. He also has stock options in Aspen Neurosciences, Inc. Benoit Tyl is employed by Bayer. Masahiro Murakami is employed by and owns stock in Eli Lilly & Company. Leslie Oley Wilberforce is employed by Evidation. Sherry Pagoto receives research funds from WW International and consulting fees from Fitbit and WW International. Ola Vedin is employed by Boehringer Ingelheim AB. Tomas Andersson is employed by and owns stock in AstraZeneca. Olveen Carrasquillo received support from NIH grants and PCORI. Dr. Carrasquillo received consulting fees from Duke University Medical Center and the University of Florida. Dr. Carrasquillo also participates in the Data Safety Monitoring Board for the NIH COPE Study and is on the Board of Directors for the Health Council of South Florida and Miami-Dade Area Health Education Center. Scott H. Kollins is employed by Akili, Inc. and owns stock in Akili, Inc. Jill Pellegrino is employed by CVS Health. Megan L. Ranney received support from NIH grants and a grant from the University of Wisconsin SMAHRT grant. Dr. Ranney also is a member of the Data Safety Monitoring Board for Project COBALT at the University of Pennsylvania., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Methods: We investigated the potential to short-term forecast COVID-19 caseloads using data from California's implementation of the Google Apple Exposure Notification (GAEN) platform, branded as CA Notify. CA Notify is a digital public health intervention leveraging resident's smartphones for anonymous EN. We extended a published statistical model that uses prior case counts to investigate the possibility of predicting short-term future case counts and then added EN activity to test for improved forecast performance. Additional predictive value was assessed by comparing the pandemic forecasting models with and without EN activity to the actual reported caseloads from 1-7 days in the future., Results: Observation of time series presents noticeable evidence for temporal association of system activity and caseloads. Incorporating earlier ENs in our model improved prediction of the caseload counts. Using Bayesian inference, we found nonzero influence of EN terms with probability one. Furthermore, we found a reduction in both the mean absolute percentage error and the mean squared prediction error, the latter of at least 5% and up to 32% when using ENs over the model without., Conclusions: This preliminary investigation suggests smartphone based ENs can significantly improve the accuracy of short-term forecasting. These predictive models can be readily deployed as local early warning systems to triage resources and interventions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Aronoff-Spencer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., Objective: This research examined apps that claimed to be useful to older adults with a goal of (1) classifying the functionality of each app, (2) identifying whether a privacy policy existed and was accessible, and (3) evaluating evidence that could support claims of value to older adults., Methods: An environmental scan was conducted using the Google search engine and typing "apps for older adults." The first 25 sites that this search returned comprised the primary data for this study. Data were organized by descriptive features of purpose (eg, health, finance, and utility), the existence of an electronically accessible privacy policy, price, and evidence supporting each recommended mobile app., Results: A total of 133 mobile apps were identified and promoted as being the best "apps for older adults." Of these 133 mobile apps, 83% (n=110) included a privacy policy. Fewer apps designated in the "medical" category included a privacy policy than those classified otherwise., Conclusions: The results suggest that most mobile apps targeting older adults include a privacy policy. Research is needed to determine whether these privacy policies are readable, succinct, and incorporate accessible data use and sharing practices to mitigate potential risks, particularly when collecting potentially sensitive health information., (©Megan Sweeney, William Barton, Camille Nebeker. Originally published in JMIR Formative Research (https://formative.jmir.org), 27.04.2023.)

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. Moreover, promoting engagement in moderate-to-vigorous PA (MVPA) may be most beneficial to brain health; thus, using mHealth to help older adults increase time spent in MVPA in free-living environments may help to offset the burden of Alzheimer's disease and related dementias and improve quality of life in older age., Objective: We developed a novel PA intervention that leverages mHealth to help older adults achieve more minutes of MVPA independently. This pilot study was a 12-week randomized controlled trial to investigate the feasibility of providing just-in-time (JIT) feedback about PA intensity during free-living exercise sessions to help older adults meet current PA recommendations (150 minutes per week of MVPA)., Methods: Participants were eligible if they were cognitively healthy English speakers aged between 65 and 80 years without major cardiovascular, neurologic, or mental health conditions; could ambulate independently; and undergo magnetic resonance imaging. Enrollment occurred from October 2017 to March 2020. Participants randomized to the PA condition received an individualized exercise prescription and an mHealth device that provided heart rate-based JIT feedback on PA intensity, allowing them to adjust their behavior in real time to maintain MVPA during exercise sessions. Participants assigned to the healthy aging education condition received a reading prescription consisting of healthy aging topics and completed weekly quizzes based on the materials., Results: In total, 44 participants were randomized to the intervention. A follow-up manuscript will describe the results of the intervention as well as discuss screening, recruitment, adverse events, and participants' opinions regarding their participation in the intervention., Conclusions: The long-term goal of this intervention is to better understand how MVPA affects brain and cognitive health in the real world and extend laboratory findings to everyday life. This pilot randomized controlled trial was conducted to determine the feasibility of using JIT heart rate zone feedback to help older adults independently increase time spent in MVPA while collecting data on the plausible mechanisms of change (frontal and medial temporal cerebral blood flow and cardiorespiratory fitness) that may affect cognition (memory and executive function) to help refine a planned stage 2 behavioral trial., Trial Registration: ClinicalTrials.gov NCT03058146; https://clinicaltrials.gov/ct2/show/NCT03058146., International Registered Report Identifier (irrid): DERR1-10.2196/42980., (©Chelsea C Hays Weeks, Alison A Moore, Matthew Allison, Kevin Patrick, Mark W Bondi, Camille Nebeker, Thomas T Liu, David Wing, Michael Higgins, Sheri J Hartman, Robert A Rissman, Zvinka Z Zlatar. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 13.02.2023.)

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. We aggregated and analyzed data from December 10, 2020, to July 1, 2021, in compliance with the CA Notify privacy policy., Results: We estimated CA Notify adoption at nearly 11 million smartphone activations during the study period. Among 1 654 201 CA Notify users who received a positive test result for SARS-CoV-2, 446 634 (27%) shared their unique code, leading to ENs for other CA Notify users who were in close proximity to the SARS-CoV-2-positive individual. We identified at least 122 970 CA Notify users as contacts through this process. Contact identification occurred a median of 4 days after symptom onset or specimen collection date of the user who received a positive test result for SARS-CoV-2., Practice Implications: Smartphone-based EN systems are promising new tools to supplement traditional contact tracing and public health interventions, particularly when efficient scaling is not feasible for other approaches. Methods to collect and interpret appropriate measures of system performance must be refined while maintaining trust and privacy.

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. A total of 61 eligible respondents completed the survey. A minority of respondents (38%) acknowledged receiving research ethics training prior to enrollment into a postgraduate pharmacy program with nearly half (16%) describing this training as informal. In comparison, a larger percentage of the total respondents (56%) had received research ethics training during their postgraduate program enrollment, with nearly half of those (25%) indicating that this training was informal. A majority of respondents reported a strong need for integrating a formal research ethics course into postgraduate pharmacy curriculum (90%) to support their research training and thesis writing (89%). Overall, the study revealed a notable lack of research ethics education for graduate-level pharmacy students in Jordan., (© 2022. The Author(s).)

Background: Mental health (MH) apps are growing in popularity. While MH apps may be helpful, less is known about how crises such as suicidal ideation are addressed in apps. Aims: We examined the proportion of MH apps that contained language mentioning suicide or suicidal ideation and how apps communicated these policies and directed users to MH resources through app content, terms of services, and privacy policies. Method: We chose apps using an Internet search of "top mental health apps," similar to how a user might find an app, and extracted information about how crisis language was presented in these apps. Results: We found that crisis language was inconsistent among apps. Overall, 35% of apps provided crisis-specific resources in their app interface and 10.5% contained crisis language in terms of service or privacy policies. Limitations: This study employed a nonsystematic approach to sampling apps, and therefore the findings may not broadly represent apps for MH. Conclusion: To address the inconsistency of crisis resources, crisis language should be included as part of app evaluation frameworks, and internationally accessible, vetted resources should be provided to app users.

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., Methods: For All of Us, research participants completed online survey forms as part of a nationwide prospective cohort study. In local EHRs, patients were selected based on diagnosis codes., Main Outcome Measures: Social determinants of health data coverage, characterized by the proportion of each disease cohort with available data regarding demographics and socioeconomic factors., Results: In All of Us, we identified 23 806 unique adult patients, of whom 2246 had a diagnosis of diabetic retinopathy, 13 448 had a diagnosis of glaucoma, 6634 had a diagnosis of cataracts, and 1478 had a diagnosis of age-related macular degeneration. Survey completion rates were high (99.5%-100%) across all cohorts for demographic information, overall health, income, education, and lifestyle. However, health care access (12.7%-29.4%), housing (0.7%-1.1%), social isolation (0.2%-0.3%), and food security (0-0.1%) showed significantly lower response rates. In local EHRs, we identified 80 548 adult patients, of whom 6616 had a diagnosis of diabetic retinopathy, 26 793 had a diagnosis of glaucoma, 40 427 had a diagnosis of cataracts, and 6712 had a diagnosis of age-related macular degeneration. High data coverage was found across all cohorts for variables related to tobacco use (82.84%-89.07%), alcohol use (77.45%-83.66%), and intravenous drug use (84.76%-93.14%). However, low data coverage (< 50% completion) was found for all other variables, including education, finances, social isolation, stress, physical activity, food insecurity, and transportation. We used chi-square testing to assess whether the data coverage varied across different disease cohorts and found that all fields varied significantly (P < 0.001)., Conclusions: The limited and highly variable data coverage in both local EHRs and All of Us highlights the need for researchers and providers to develop SDoH data collection strategies and to assemble complete datasets.

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., Objective: The aim of this study was to evaluate the applicability of automated analysis of the drawing process for estimating global cognition in community-dwelling older adults across populations in different nations., Methods: We collected drawing data with a digital tablet, along with Montreal Cognitive Assessment (MoCA) scores for assessment of global cognition, from 92 community-dwelling older adults in the United States and Japan. We automatically extracted 6 drawing features that characterize the drawing process in terms of the drawing speed, pauses between drawings, pen pressure, and pen inclinations. We then investigated the association between the drawing features and MoCA scores through correlation and machine learning-based regression analyses., Results: We found that, with low MoCA scores, there tended to be higher variability in the drawing speed, a higher pause:drawing duration ratio, and lower variability in the pen's horizontal inclination in both the US and Japan data sets. A machine learning model that used drawing features to estimate MoCA scores demonstrated its capability to generalize from the US dataset to the Japan dataset (R 2 =0.35; permutation test, P<.001)., Conclusions: This study presents initial empirical evidence of the capability of automated analysis of the drawing process as an estimator of global cognition that is applicable across populations. Our results suggest that such automated analysis may enable the development of a practical tool for international use in self-administered, automated cognitive assessment., (©Yasunori Yamada, Kaoru Shinkawa, Masatomo Kobayashi, Varsha D Badal, Danielle Glorioso, Ellen E Lee, Rebecca Daly, Camille Nebeker, Elizabeth W Twamley, Colin Depp, Miyuki Nemoto, Kiyotaka Nemoto, Ho-Cheol Kim, Tetsuaki Arai, Dilip V Jeste. Originally published in JMIR Formative Research (https://formative.jmir.org), 05.05.2022.)

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.

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. Researchers need to be involved in the risk assessment process, as many tools in the marketplace (eg, wellness apps, fitness sensors) are underregulated. However, there is little guidance to assist researchers and institutional review boards in their evaluation of digital tools for research purposes. To address this gap, the Digital Health Checklist for Researchers (DHC-R) was developed as a decision support tool. A participatory research approach involving a group of behavioral scientists was used to inform DHC-R development. Scientists beta-tested the checklist by retrospectively evaluating the technologies they had chosen for use in their research. This paper describes the lessons learned because of their involvement in the beta-testing process and concludes with recommendations for how the DHC-R could be useful for a variety of digital health stakeholders. Recommendations focus on future research and policy development to support research ethics, including the development of best practices to advance safe and responsible digital health research., (©Rebecca Bartlett Ellis, Julie Wright, Lisa Soederberg Miller, Danielle Jake-Schoffman, Eric B Hekler, Carly M Goldstein, Danielle Arigo, Camille Nebeker. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.12.2021.)

Introduction: 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. Methods: 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. We examined: (1) associations of NLP-derived assessments of social relationships and linguistic features with validated self-report assessments of social support and loneliness; and (2) important linguistic features for detecting individuals with higher level of SI/L by using machine learning (ML) models. Results: NLP-derived assessments of social relationships were associated with self-reported assessments of social support and loneliness, though these associations were stronger in women than in men. Usage of first-person plural pronouns was negatively associated with loneliness in women and positively associated with emotional support in men. ML analysis using leave-one-out methodology showed good performance (F1 = 0.73, AUC = 0.75, specificity = 0.76, and sensitivity = 0.69) of the binary classification models in detecting individuals with higher level of SI/L. Comparable performance were also observed when classifying social and emotional support measures. Using ML models, we identified several linguistic features (including use of first-person plural pronouns, sentiment, sentence complexity, and sentence similarity) that most strongly predicted scores on scales for loneliness and social support. Discussion: Linguistic data can provide unique insights into SI/L among older adults beyond scale-based assessments, though there are consistent gender differences. Future research studies that incorporate diverse linguistic features as well as other behavioral data-streams may be better able to capture the complexity of social functioning in older adults and identification of target subpopulations for future interventions. Given the novelty, use of NLP should include prospective consideration of bias, fairness, accountability, and related ethical and social implications., Competing Interests: KS, YY, and H-CK are employees of IBM. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Badal, Nebeker, Shinkawa, Yamada, Rentscher, Kim and Lee.)

Background: 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. Within the context of a physical activity intervention, this study gathered participant's opinions about the use of an mHealth device to learn about acceptance and to identify areas for improvement. Methods: The Independent Walking for Brain Health study (NCT03058146) was designed to evaluate the effectiveness of a wearable mHealth technology in facilitating adherence to a physical activity prescription among participants in free-living environments. An Exit Survey was conducted following intervention completion to gauge participant's perceptions and solicit feedback regarding the overall study design, including exercise promotion strategies and concerns specific to the technology (e.g., privacy), that could inform more acceptable mHealth interventions in the future. The Digital Health Checklist and Framework was used to guide the analysis focusing on the domains of Privacy, Access and Usability, and Data Management. Results: Participants ( n = 41) were in their early 70's (mean = 71.6) and were predominantly female (75.6%) and White (92.7%). Most were college educated (16.9 years) and enjoyed using technology in their everyday life (85.4%). Key challenges included privacy concerns, device accuracy, usability, and data access. Specifically, participants want to know what is being learned about them and want control over how their identifiable data may be used. Overall, participants were able to use the device despite the design challenges. Conclusions: Understanding participant's perceptions of the challenges and concerns introduced by mHealth is important, as acceptance will influence adoption and adherence to the study protocol. While this study learned from participants at studycompletion, we recommend that researchers consider what might influence participant acceptance of the technology (access, data management, privacy, risks) and build these into the mHealth study design process. We provide recommendations for future mHealth studies with older adults., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Nebeker and Zlatar.)

Background: 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. Informed consent documents to participate in research are lengthy and difficult for prospective participants to read and understand. As the consent communication will need to include concepts and procedures unique to digital health research, making that information accessible and meaningful to the prospective participant is critical for consent to be informed. This paper describes a methodology that researchers can apply when developing a consent communication for digital health research. Methods: A consent document approved by a US institutional review board was deconstructed into segments that aligned with federal requirements for informed consent. Three researchers independently revised each segment of text with a goal of achieving a readability score between a 6-8th grade level. The team then consulted with an external readability expert well-versed in revising informed consent documents into "plain language." The resulting text was evaluated using Microsoft Word and Online-Utility accessibility software. The final step involved adding visual images and graphics to complement the text. The Digital Health Checklist consent prototype builder was then used to identify areas where the consent content could be expanded to address four key domains of Access and Usability, Privacy, Risks and Benefits, and Data Management. Results: The approved consent was evaluated at a 12.6 grade reading level, whereas the revised language by our study team received 12.4, 12, and 12.58, respectively. The final consent document synthesized the most readable of the three revised versions and was further revised to include language recommended by the software tool for improving readability, which resulted in a final revised consent readability score of a 9.2 grade level. Moreover, word count was reduced from 6,424 in the original consent to 679 in the rewritten consent form. Conclusion: Utilizing an iterative process to design an accessible informed consent document is a first step in achieving meaningful consent to participate in digital health research. This paper describes how a consent form approved by an institutional review board can be made more accessible to a prospective research participant by improving the document readability score, reducing the word count and assessing alignment with the Digital Health Checklist., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Nebeker, Gholami, Kareem and Kim.)

Background. The community health worker (CHW) model is utilized globally to promote health and reduce health disparities in hard-to-reach and underserved communities. The model is deemed successful due to involvement of these natural helpers who are familiar with the local customs, language, and traditions. "Research" CHWs (also known as promotores ) serve as cultural mediators between their community and academic researchers and are increasingly involved in the design and implementation of research; yet few of these individuals have received formal training in research methods or ethics. This study identified requisite skills and knowledge needed by research CHWs. Method. Investigators who utilized the CHW/promotor model were recruited to complete a survey and participate in one of four focus group sessions. Participants identified (1) research roles, (2) training received, (3) research competencies, (4) training barriers and facilitators, and (5) assessment preferences. Results. Participants ( n = 20) completed a survey with 19 also participating in a focus group session. All participants involved CHWs in research implementation, with nearly half involving CHWs in the study design and/or dissemination of findings phases. Critical thinking skills and application of ethical principles (e.g., demonstrating respect) were prioritized over knowledge of research infrastructure (e.g., institutional review board/ethics review process). Research ethics training designed for academic researchers was deemed inappropriate because sophisticated terminology and web-based delivery were perceived as an access barrier. Self-assessment and contextualized scenarios were recommended to assess critical thinking. Conclusions. Researchers using the CHW model should provide relevant and accessible research competency training.

A major obstacle to mental health treatment for many Americans is accessibility: the United States faces a shortage of mental health providers, resulting in federally designated shortage areas. Although digital mental health treatments (DMHTs) are effective interventions for common mental disorders, they have not been widely adopted by the U.S. health care system. National and international expert stakeholders representing health care organizations, insurance companies and payers, employers, patients, researchers, policy makers, health economists, and DMHT companies and the investment community attended two Banbury Forum meetings. The Banbury Forum reviewed the evidence for DMHTs, identified the challenges to successful and sustainable implementation, investigated the factors that contributed to more successful implementation internationally, and developed the following recommendations: guided DMHTs should be offered to all patients experiencing common mental disorders, DMHT products and services should be reimbursable to support integration into the U.S. health care landscape, and an evidence standards framework should be developed to support decision makers in evaluating DMHTs.

Background: Ethics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts., Main Text: In this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC's scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC's way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science., Conclusions: We believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large.

Background: As of March 2021, in the USA, the COVID-19 pandemic has resulted in over 500,000 deaths, with a majority being people over 65 years of age. Since the start of the pandemic in March 2020, preventive measures, including lockdowns, social isolation, quarantine, and social distancing, have been implemented to reduce viral spread. These measures, while effective for risk prevention, may contribute to increased social isolation and loneliness among older adults and negatively impact their mental and physical health., Objective: This study aimed to assess the impact of the COVID-19 pandemic and the resulting "Stay-at-Home" order on the mental and physical health of older adults and to explore ways to safely increase social connectedness among them., Methods: This qualitative study involved older adults living in a Continued Care Senior Housing Community (CCSHC) in southern California, USA. Four 90-minute focus groups were convened using the Zoom Video Communications platform during May 2020, involving 21 CCSHC residents. Participants were asked to describe how they were managing during the "stay-at-home" mandate that was implemented in March 2020, including its impact on their physical and mental health. Transcripts of each focus group were analyzed using qualitative methods., Results: Four themes emerged from the qualitative data: (1) impact of the quarantine on health and well-being, (2) communication innovation and technology use, (3) effective ways of coping with the quarantine, and (4) improving access to technology and training. Participants reported a threat to their mental and physical health directly tied to the quarantine and exacerbated by social isolation and decreased physical activity. Technology was identified as a lifeline for many who are socially isolated from their friends and family., Conclusions: Our study findings suggest that technology access, connectivity, and literacy are potential game-changers to supporting the mental and physical health of older adults and must be prioritized for future research., (©Jessica R Daly, Colin Depp, Sarah A Graham, Dilip V Jeste, Ho-Cheol Kim, Ellen E Lee, Camille Nebeker. Originally published in JMIR Aging (http://aging.jmir.org), 22.03.2021.)

The importance of research ethics (RE) training has led academic and funding institutions to require that students, trainees, and faculty obtain such training at various stages of their careers. Despite the increasing awareness of the value RE education offers, this training requirement is absent in Jordan. We aimed to assess RE education offerings of pharmacy master programs in Jordan and compare with the top-ranked pharmacy graduate programs globally. Therefore, a list of universities that offer research-based pharmacy master programs was created. Each program was evaluated for the inclusion of RE education. A qualitative content analysis approach based on inductive reasoning and latent analysis was followed to analyze the data. Results of the study showed a lack of appropriate RE education for graduate-level pharmacy programs in Jordan with only 40% of the programs partially discuss selected topics related to RE. Regarding pharmacy graduate programs globally, 10% offer a standalone RE course, 40% offer some discussions related to RE, another 10% do not offer RE education in any form, and the remaining 40% of the programs were difficult to assess due to lack of sufficient information available online. Based on the findings of this study, training in RE is tends to be lacking in pharmacy graduate programs in Jordan and globally, with a greater lack in Jordan than globally. There is a need to incorporate formal RE education into programs that do not offer this type of instruction. Programs that formally touch on some aspects of RE need to expand the scope of topics to include more RE-related themes. Integrating a standalone RE course into pharmacy graduate programs is highly encouraged., Competing Interests: The authors have declared that no competing interests exist.

This manuscript is the result of an interdisciplinary team approach to examine the ethical and cultural considerations of biospecimen collection among American Indian and Alaskan Native (AIAN) communities for the planned Healthy Brain and Child Development (HBCD) study. We begin by reviewing a brief history of the treatment of AIAN communities by the US government and within research studies. Based in part on this history, we highlight the overlapping and intersecting vulnerabilities of AIAN communities, including historical trauma, poverty, lack of healthcare access, and environmental hazards. After consideration of ethical and legal implications, we introduce our recommendations for biospecimen collection/biobanking with AIAN communities in the context of population-representative, multi-site, national studies. We recommend the following key considerations: (1) authentic partnership development; (2) beneficence to the community; (3) culturally respectful research design; (4) meaningful consent to support enrollment and retention; (5) culturally appropriate data management. Adherence to a culturally aware approach for inclusion of underrepresented communities assures external validity in the national studies and increases likelihood of bidirectional value exchange., Competing Interests: Compliance with Ethical Standards Conflict of Interest Within the past year, Dr. Pilar Ossorio has undertaken paid consulting work concerning research ethics issues for Roche-Genentech and Eli Lilly. None of this work involved recruitment of or relationships with indigenous communities. Other co-authors: None.

The speed and scale of the COVID-19 pandemic has highlighted the limits of current health systems and the potential promise of non-establishment research such as "DIY" research. We consider one example of how DIY research is responding to the pandemic, discuss the challenges faced by DIY research more generally, and suggest that a "trust architecture" should be developed now to contribute to successful future DIY efforts., (© 2020 The Hastings Center.)

Background: In 2015, Collins and Varmus articulated a vision for precision medicine emphasizing molecular characterization of illness to identify actionable biomarkers to support individualized treatment. Researchers have argued for a broader conceptualization, precision health. Precision health is an ambitious conceptualization of health, which includes dynamic linkages between research and practice as well as medicine, population health, and public health. The goal is a unified approach to match a full range of promotion, prevention, diagnostic, and treatment interventions to fundamental and actionable determinants of health; to not just address symptoms, but to directly target genetic, biological, environmental, and social and behavioral determinants of health., Purpose: The purpose of this paper is to elucidate the role of social and behavioral sciences within precision health., Main Body: Recent technologies, research frameworks, and methods are enabling new approaches to measure, intervene, and conduct social and behavioral science research. These approaches support three opportunities in precision health that the social and behavioral sciences could colead including: (a) developing interventions that continuously "tune" to each person's evolving needs; (b) enhancing and accelerating links between research and practice; and (c) studying mechanisms of change in real-world contexts. There are three challenges for precision health: (a) methods of knowledge organization and curation; (b) ethical conduct of research; and (c) equitable implementation of precision health., Conclusions: Precision health requires active coleadership from social and behavioral scientists. Prior work and evidence firmly demonstrate why the social and behavioral sciences should colead with regard to three opportunity and three challenge areas., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society of Behavioral Medicine.)

Digital technologies offer researchers new approaches to test personalized and adaptive health interventions tailored to an individual. Yet, research leveraging technologies to capture personal health data involve technical and ethical consideration during the study design phase. No guidance exists to facilitate responsible digital technology selection for research purposes. A stakeholder-engaged and iterative approach was used to develop, test, and refine a checklist designed to aid researchers in selecting technologies for their research. First, stakeholders (n = 7) discussed and informed key decision-making domains to guide app/device selection derived from the American Psychiatric Association's framework that included safety, evidence, usability, and interoperability. We added "ethical principles" to the APA's hierarchical model and created a checklist that was used by a small group of behavioral scientists (n = 7). Findings revealed the "ethical principles" domains of respect, beneficence, and justice cut across each decision-making domains and the checklist questions/prompts were revised accordingly and can be found at thecore.ucsd.edu. The refined checklist contains four decision-making domains with prompts/questions and ethical principles embedded within the domains of privacy, risk/benefit, data management, and access/evidence. This checklist is the first step in leading the narrative of decision-making when selecting digital health technologies for research. Given the dynamic and rapidly evolving nature of digital health technology use in research, this tool will need to be further evaluated for usefulness in technology selection., (© Society of Behavioral Medicine 2019. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

"Respect for Persons" is an ethical principle demonstrated through the informed consent process. Participants at a large HIV research center were surveyed to identify important aspects of the consent process. Persons with and without HIV ( n  = 103) completed a short pre/post questionnaire with both open-ended and forced choice response options. Qualitative analysis resulted in eleven themes about the most important consent elements which did not differ by HIV serostatus. Overall, participants rated the informed consent content and presentation by research staff as "extremely informative" and found the consent information to be "extremely consistent" with their study experience. Study results support the value of an interactive process and can be used to inform the design of a standardized, digital consent process.

The World Health Organization (WHO) estimates older adults aged 60+ will double by 2050 with 80% living in low to moderate income countries. As remote research studies supported by digital devices increase separation between researchers and participants, it is important to maintain participant trust. Research participants have expressed an interest in accessing both group and individual level results, which are not readily available. To bridge this gap, we engaged residents of a local continuing care senior housing community (CCSHC) to co-design documents used to convey information about study results. The process informed the refinement of informational materials for communicating scientific research that the CCSHC community considers accessible and meaningful.

Background: Access to digital health technologies is contributing to a paradigm shift where sick care may become authentic health care. Individuals can now access personal health data through wearable sensors, affordable lab screenings, genetic and genomic sequencing, and real-time health tracking apps. Personal health data access creates opportunities to study health indicators 24/7 and in real time. This is especially useful for patients with hard-to-diagnose or treat diseases, which led to a self-formed patient group called Project Apollo. Project Apollo is composed of highly motivated patients with common experiences of undiagnosed conditions, a lack of clear treatment options, and shared frustrations with navigating the U.S. healthcare system. These experiences have led the Apollo cohort to supplement their health knowledge through self-study research. Objective: To qualify the experience and expectations of patients affiliated with Project Apollo. Methods: A qualitative approach involved record review and semi-structured interviews. One-hour semi-structured interviews were conducted to solicit motivations, expectations, and potential barriers and facilitators to self-study followed by a brief survey on digital tool use. Interviews were digitally recorded, transcribed, and analyzed to identify themes and patterns. Results: Participants included six females and three males ranging in age from 30 to 70+ years. Responses were organized under five key themes including: frustration with healthcare system; community support; self-study/N-of-1 research; access to experts; moving from sick to healthcare. Facilitators include motivation, albeit stemming from frustration, a safe community where patients derive support, and access to experts for guidance. Increasing awareness of clinicians about the potential value of partnering with patients who are advancing health knowledge through self-study is critical. Conclusions: N-of-1 self-study research, coupled with community support and digital health tools, appears to be one plausible pathway to shifting the paradigm from sick care toward patient-partnered health care., (Copyright © 2020 Nebeker, Weisberg, Hekler and Kurisu.)

Behavioral scientists are developing new methods and frameworks that leverage mobile health technologies to optimize individual level behavior change. Pervasive sensors and mobile apps allow researchers to passively observe human behaviors "in the wild" 24/7 which supports delivery of personalized interventions in the real-world environment. This is all possible because these technologies contain an incredible array of sensors that allow applications to constantly record user location and can contextualize current environmental conditions through barometers, thermometers, and ambient light sensors and can also capture audio and video of the user and their surroundings through multiple integrated high-definition cameras and microphones. These tools are a game changer in behavioral health research and, not surprisingly, introduce new ethical, regulatory/legal and social implications described in this article.

The development and use of large and dynamic health data repositories designed to support research pose challenges to traditional informed consent models. We used semi-structured interviewing (n=44) to elicit diverse research stakeholders' views of a model of consent appropriate to participation in initiatives that entail collection, long-term storage, and undetermined future research use of multiple types of health data. We demonstrate that, when considering health data repositories, research stakeholders replace a concept of consent as informed with one in which consent is engaged. In engaged consent, a participant's ongoing relationship with a repository serves as a substitute or adjunct to information exchange at enrollment. We detail research stakeholders' views of the risks of engaged consent and suggest questions for further study about engagement and consent procedures in initiatives that aim to store data for future unspecified research purposes.

Research is needed on interventions targeting sedentary behavior with appropriate behavior-change tools. The current study used convergent sequential mixed methods (QUAN + qual) to explore tool use during a edentary behavior intervention. Data came from a two-arm randomized sedentary behavior pilot intervention. Participants used a number of intervention tools (e.g., prompts and standing desks). Separate mixed-effects regression models explored associations between change in number of tools and frequency of tool use with two intervention targets: change in sitting time and number of sit-to-stand transitions overtime. Qualitative data explored participants' attitudes towards intervention tools. There was a significant relationship between change in total tool use and sitting time after adjusting for number of tools (β = -12.86, p = .02), demonstrating that a one-unit increase in tool use was associated with an almost 13 min reduction in sitting time. In contrast, there was a significant positive association between change in number of tools and sitting time after adjusting for frequency of tool use (β = 63.70, p = .001), indicating that increasing the number of tools without increasing frequency of tool use was associated with more sitting time. Twenty-four semistructured interviews were coded and a thematic analysis revealed four themes related to tool use: (a) prompts to disrupt behavior; (b) tools matching the goal; (c) tools for sit-to-stand were ineffective; and (d) tool use evolved over time. Participants who honed in on effective tools were more successful in reducing sitting time. Tools for participants to increase sit-to-stand transitions were largely ineffective. This study is registered at clincialtrials.gov. Identifier: NCT02544867., (© Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Mobile health (mHealth) research involving pervasive sensors, mobile apps and other novel data collection tools and methods present new ethical, legal, and social challenges specific to informed consent, data management and bystander rights. To address these challenges, a participatory design approach was deployed whereby stakeholders contributed to the development of a web-based commons to support the mHealth research community including researchers and ethics board members. The CORE (Connected and Open Research Ethics) platform now features a community forum, a resource library and a network of nearly 600 global members. The utility of the participatory design process was evaluated by analyzing activities carried out over an 8-month design phase consisting of 86 distinct events including iterative design deliberations and social media engagement. This article describes how participatory design yielded 55 new features directly mapped to community needs and discusses relationships to user engagement as demonstrated by a steady increase in CORE member activity and followers on Twitter.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders., Competing Interests: Declaration of Competing Interest Authors YY and HK are employees of IBM. The other authors have no conflicts of interest to report., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Objective: Social network platforms are increasingly used in digital health research. Our study aimed to 1. qualify and quantify the use of social media platforms in health research supported by the National Institutes of Health (NIH) and document changes occurring between 2011 and 2017 and 2. examine whether institutions hosting these studies provided public-facing guidelines on how to conduct ethical social media health research., Methods: The NIH RePORTER (Research Portfolio Online Reporting Tools) database was searched to identify research utilizing Instagram, Pinterest, Facebook, or Twitter. Studies included used social media for observational research, recruitment, intervention delivery or to assess social media as an effective research tool. Abstracts were qualitatively analyzed to describe the population and health topic by year. Websites of organizations receiving funding for this research were searched to identify whether guidance or policy existed., Results: Studies ( n  = 105) were organized by population targeted and health focus. Main "Health" themes were labeled: 1. substance use, 2. disease/diagnosis, 3. psychiatry/mental health, and 4. weight and physical activity. The populations most involved included adolescents and young adults, and men who have sex with men. The number of research studies using social media increased approximately 590% between 2011 and 2017. Studies were linked to 56 organizations of which 21% ( n  = 12) provided some accessible guidance with 79% ( n  = 44) offering no guidance specific to social media health research., Conclusions: Social media research is conducted with vulnerable populations that are traditionally difficult to reach. There is a compelling need for resources designed to support ethical and responsible social media-enabled research to enable this research to be carried out safely., (© The Author(s) 2020.)

Background: With the rise of precision medicine efforts worldwide, our study objective was to describe and map the emerging precision medicine landscape. A Google search was conducted between June 19, 2017 to July 20, 2017 to examine how "precision medicine" and its analogous terminology were used to describe precision medicine efforts. Resulting web-pages were reviewed for geographic location, data type(s), program aim(s), sample size, duration, and the key search terms used and recorded in a database. Descriptive statistics were applied to quantify terminology used to describe specific precision medicine efforts. Qualitative data were analyzed for content and patterns., Results: Of the 108 programs identified through our search, 84% collected only biospecimen(s) and, of those that collected at least two data types, 42% mentioned both Electronic Health Records (EHR) and biospecimen. Given the majority of efforts limited to biospecimen(s) use, genetic research seems to be prioritized in association with precision medicine. Roughly, 54% were found to collect two or more data types, which limits the output of information that may contribute to understanding of the interplay of genetic, lifestyle, and environmental factors. Over half were government-funded with roughly a third being industry-funded. Most initiatives were concentrated in the United States, Europe, and Asia., Conclusions: To our knowledge, this is the first study to map and qualify the global precision medicine landscape. Our findings reveal that precision medicine efforts range from large model cohort studies involving multidimensional, longitudinal data to biorepositories with a collection of blood samples. We present a spectrum where past, present, and future PM-like efforts can fall based on their scope and potential impact. If precision medicine is based on genes, lifestyle and environmental factors, we recommend programs claiming to be precision medicine initiatives to incorporate multidimensional data that can inform a holistic approach to healthcare.

Purpose of Review: Artificial intelligence (AI) technology holds both great promise to transform mental healthcare and potential pitfalls. This article provides an overview of AI and current applications in healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering its current limitations, areas needing additional research, and ethical implications regarding AI technology., Recent Findings: We reviewed 28 studies of AI and mental health that used electronic health records (EHRs), mood rating scales, brain imaging data, novel monitoring systems (e.g., smartphone, video), and social media platforms to predict, classify, or subgroup mental health illnesses including depression, schizophrenia or other psychiatric illnesses, and suicide ideation and attempts. Collectively, these studies revealed high accuracies and provided excellent examples of AI's potential in mental healthcare, but most should be considered early proof-of-concept works demonstrating the potential of using machine learning (ML) algorithms to address mental health questions, and which types of algorithms yield the best performance. As AI techniques continue to be refined and improved, it will be possible to help mental health practitioners re-define mental illnesses more objectively than currently done in the DSM-5, identify these illnesses at an earlier or prodromal stage when interventions may be more effective, and personalize treatments based on an individual's unique characteristics. However, caution is necessary in order to avoid over-interpreting preliminary results, and more work is required to bridge the gap between AI in mental health research and clinical care.

Background: There is a growing interest in creating large-scale repositories that store genetic, behavioral, and environmental data for future, unspecified uses. The All of Us Research Program is one example of such a repository. Its participants will get access to their personal data and the results of the studies that used them. However, little is known about what researchers should return to participants and how they should do it in a way that is valuable and meaningful to participants. Methods: To better understand the concept of "return of value" and the practice of returning valuable study information, we conducted semi-structured telephone interviews with 44 stakeholders with diverse perspectives on this topic. All interviews have been transcribed and coded thematically to identify the most salient themes, to explore differences between returning different types of study results, and to describe differences and similarities in perspectives of different stakeholder groups. Results: We found that one size does not fit all when it comes to returning value to participants: the decisions about return of results are affected by participant preferences, researchers' concerns about feasibility, the types of data collected, their level of granularity, and available options for supporting result interpretation. Conclusions: Our findings suggest that the key to operationalizing return of value and to identifying ways to return valuable information to study participants may be to find a point of equilibrium between criteria that may affect usefulness and feasibility. The point of equilibrium may vary by study, by participants' backgrounds and preferences, by their health literacy and access to regular healthcare, and by the resources available to professionals controlling the data. Future studies should explore the factors that determine the point of equilibrium between feasibility and usefulness.

Objective: To examine associations of sociodemographic and clinical factors with cognitive, physical, and mental health among independent living older adults in a continuing care senior housing community (CCSHC)., Methods: This was a cross-sectional study at the independent living sector of a CCSHC in San Diego County, California. Participants included English-speaking adults aged 65-95 years, of which two-thirds were women. Of the 112 subjects recruited, 104 completed basic study assessments. The authors computed composite measures of cognitive, physical, and mental health. The authors also assessed relevant clinical correlates including psychosocial factors such as resilience, loneliness, wisdom, and social support., Results: The CCSHC residents were similar to a randomly selected community-based sample of older adults on most standardized clinical measures. In the CCSHC, physical health correlated with both cognitive function and mental health, but there was no significant correlation between cognitive and mental health. Cognitive function was significantly associated with physical mobility, satisfaction with life, and wisdom, whereas physical health was associated with age, self-rated physical functioning, mental well-being, and resilience. Mental health was significantly associated with income, optimism, self-compassion, loneliness, and sleep disturbances., Conclusion: Different psychosocial factors are significantly associated with cognitive, physical, and mental health. Longitudinal studies of diverse samples of older adults are necessary to determine risk factors and protective factors for specific domains of health. With rapidly growing numbers of older adults who require healthcare as well as supportive housing, CCSHCs will become increasingly important sites for studying and promoting the health of older adults., (Copyright © 2019 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.)