BACKGROUND: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items.
OBJECTIVE: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app.
METHODS: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants\' perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses.
RESULTS: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants\' perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory.
CONCLUSIONS: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains.

BACKGROUND: Physicians and patient-facing caregivers have increasingly used mobile health (mHealth) technologies in the past several years, accelerating during the COVID-19 pandemic. However, barriers and feedback surrounding adoption remain relatively understudied and varied across health systems, particularly in rural areas.
OBJECTIVE: This study aims to identify provider adoption, attitudes, and barriers toward mHealth in a large, multisite, rural US health care system. We investigated (1) mHealth apps that providers use for their own benefit and (2) mHealth apps that a provider uses in conjunction with a patient.
METHODS: We surveyed all patient-seeing providers within the Marshfield Clinic Health System with a brief, 16-item, web-based survey assessing attitudes toward mHealth, adoption of these technologies, and perceived barriers faced by providers, their peers, and the institution. Survey results were summarized via descriptive statistics, with log-binomial regression and accompanying pairwise analyses, using Kruskal-Wallis and Jonckheere-Terpstra tests for significance, respectively. Respondents were grouped by reported clinical role and specialty.
RESULTS: We received a 38% (n/N=916/2410) response rate, with 60.7% (n=556) of those sufficiently complete for analyses. Roughly 54.1% (n=301) of respondents reported mHealth use, primarily around decision-making and supplemental information, with use differing based on provider role and years of experience. Self-reported barriers to using mHealth included a lack of knowledge and time to study mHealth technologies. Providers also reported concerns about patients\' internet access and the complexity of mHealth apps to adequately use mHealth technologies. Providers believed the health system\'s barriers were largely privacy, confidentiality, and legal review concerns.
CONCLUSIONS: These findings echo similar studies in other health systems, surrounding providers\' lack of time and concerns over privacy and confidentiality of patient data. Providers emphasized concerns over the complexity of these technologies for their patients and concerns over patients\' internet access to fully use mHealth in their delivery of care.

BACKGROUND: Digitally assisted health care services and technologies are gaining popularity. They assist patients in managing their conditions, thereby reducing the burden on health care staff. Digital health care enables individuals to receive care that is more tailored to their needs and preferences. When implemented properly, it can promote equity by considering each person\'s opportunities and limitations in the context of health care needs, preferences, values, and capabilities.
OBJECTIVE: This study aims to understand the needs, values, and preferences of individuals with chronic obstructive pulmonary disease (COPD) who are provided with a 24/7 digital health care service. Furthermore, we aim to understand the dynamics of the communities to which they belong and how these communities intersect. This will provide us with the essential knowledge to establish new methods of providing education, including the development of educational activities for health professionals to engage, train, and empower people living with COPD.
METHODS: The study included 7 informants diagnosed with COPD who received 24/7 digital health care service support from a regional project in Region Zealand, Denmark. The informants were visited 4 times during 2 months, including a \"Hello\" visit, a day with a semistructured interview, and 2 days with field observations. The informants participated in a semistructured interview, following participant observation and an ethnographic approach. The interview content was analyzed using an inductive methodology to categorize the empirical data.
RESULTS: Using the inductive approach, we identified 3 main categories related to the informants\' needs, values, and preferences: (1) Health, (2) Value Creation, and (3) Resources. These 3 main categories were based on 9 subcategories: (1) health and barriers, (2) self-monitoring, (3) medication, (4) behavior, (5) motivation, (6) hobbies, (7) social networks, (8) health professionals, and (9) technology. These findings revealed that the informants placed value on maintaining their daily activities and preserving their sense of identity before the onset of COPD. Furthermore, they expressed a desire not to be defined by their COPD, as conversations about COPD often shifted away from the topic.
CONCLUSIONS: Digital health solutions and the health care professionals who offer them should prioritize the individuals they serve, considering their needs, values, and preferences rather than solely focusing on the medical condition. This approach ensures the highest level of daily living and empowerment for those living with long-term health conditions. The communities surrounding individuals must engage in constant interaction and collaboration. They should work together to incorporate people\'s needs, values, and preferences into future digital health services, thereby promoting empowerment and self-management. New educational programs aimed at developing the digital health service competencies of registered nurses should facilitate collaboration between the 2 communities. This collaboration is essential for supporting patients with long-term health conditions in their daily activities.

BACKGROUND: Vaccines serve as a crucial public health tool, although vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations.
OBJECTIVE: This study aimed to create a real-time, natural language processing (NLP)-based tool to assess vaccine sentiment and hesitancy across 3 prominent social media platforms.
METHODS: We mined and curated discussions in English from Twitter (subsequently rebranded as X), Reddit, and YouTube social media platforms posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus; measles, mumps, and rubella; and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative and to classify vaccine hesitancy using the World Health Organization\'s (WHO) 3Cs (confidence, complacency, and convenience) hesitancy model, conceptualizing an online dashboard to illustrate and contextualize trends.
RESULTS: We compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and from 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines.
CONCLUSIONS: Our innovative system performs real-time analysis of sentiment and hesitancy on 3 vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.

BACKGROUND: The high number of unnecessary alarms in intensive care settings leads to alarm fatigue among staff and threatens patient safety. To develop and implement effective and sustainable solutions for alarm management in intensive care units (ICUs), an understanding of staff interactions with the patient monitoring system and alarm management practices is essential.
OBJECTIVE: This study investigated the interaction of nurses and physicians with the patient monitoring system, their perceptions of alarm management, and smart alarm management solutions.
METHODS: This explorative qualitative study with an ethnographic, multimethods approach was conducted in an ICU of a German university hospital. Using triangulation in data collection, 102 hours of field observations, 12 semistructured interviews with ICU staff members, and the results of a participatory task were analyzed. The data analysis followed an inductive, grounded theory approach.
RESULTS: Nurses and physicians reported interacting with the continuous vital sign monitoring system for most of their work time and tasks. There were no established standards for alarm management; instead, nurses and physicians stated that alarms were addressed through ad hoc reactions, a practice they viewed as problematic. Staff members\' perceptions of intelligent alarm management varied, but they highlighted the importance of understandable and traceable suggestions to increase trust and cognitive ease.
CONCLUSIONS: Staff members\' interactions with the omnipresent patient monitoring system and its alarms are essential parts of ICU workflows and clinical decision-making. Alarm management standards and workflows have been shown to be deficient. Our observations, as well as staff feedback, suggest that changes are warranted. Solutions for alarm management should be designed and implemented with users, workflows, and real-world data at the core.

BACKGROUND: The use of artificial intelligence (AI) for pain assessment has the potential to address historical challenges in infant pain assessment. There is a dearth of information on the perceived benefits and barriers to the implementation of AI for neonatal pain monitoring in the neonatal intensive care unit (NICU) from the perspective of health care professionals (HCPs) and parents. This qualitative analysis provides novel data obtained from 2 large tertiary care hospitals in Canada and the United Kingdom.
OBJECTIVE: The aim of the study is to explore the perspectives of HCPs and parents regarding the use of AI for pain assessment in the NICU.
METHODS: In total, 20 HCPs and 20 parents of preterm infants were recruited and consented to participate from February 2020 to October 2022 in interviews asking about AI use for pain assessment in the NICU, potential benefits of the technology, and potential barriers to use.
RESULTS: The 40 participants included 20 HCPs (17 women and 3 men) with an average of 19.4 (SD 10.69) years of experience in the NICU and 20 parents (mean age 34.4, SD 5.42 years) of preterm infants who were on average 43 (SD 30.34) days old. Six themes from the perspective of HCPs were identified: regular use of technology in the NICU, concerns with regard to AI integration, the potential to improve patient care, requirements for implementation, AI as a tool for pain assessment, and ethical considerations. Seven parent themes included the potential for improved care, increased parental distress, support for parents regarding AI, the impact on parent engagement, the importance of human care, requirements for integration, and the desire for choice in its use. A consistent theme was the importance of AI as a tool to inform clinical decision-making and not replace it.
CONCLUSIONS: HCPs and parents expressed generally positive sentiments about the potential use of AI for pain assessment in the NICU, with HCPs highlighting important ethical considerations. This study identifies critical methodological and ethical perspectives from key stakeholders that should be noted by any team considering the creation and implementation of AI for pain monitoring in the NICU.

BACKGROUND: Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient populations, and clinical procedures. One barrier for successful innovation is the scarcity of research in the current literature seeking and analyzing the views of AI or ML researchers and physicians to support ethical guidance.
OBJECTIVE: This study aims to describe, using a qualitative approach, the landscape of ethical issues that AI or ML researchers and physicians with professional exposure to AI or ML tools observe or anticipate in the development and use of AI and ML in medicine.
METHODS: Semistructured interviews were used to facilitate in-depth, open-ended discussion, and a purposeful sampling technique was used to identify and recruit participants. We conducted 21 semistructured interviews with a purposeful sample of AI and ML researchers (n=10) and physicians (n=11). We asked interviewees about their views regarding ethical considerations related to the adoption of AI and ML in medicine. Interviews were transcribed and deidentified by members of our research team. Data analysis was guided by the principles of qualitative content analysis. This approach, in which transcribed data is broken down into descriptive units that are named and sorted based on their content, allows for the inductive emergence of codes directly from the data set.
RESULTS: Notably, both researchers and physicians articulated concerns regarding how AI and ML innovations are shaped in their early development (ie, the problem formulation stage). Considerations encompassed the assessment of research priorities and motivations, clarity and centeredness of clinical needs, professional and demographic diversity of research teams, and interdisciplinary knowledge generation and collaboration. Phase-1 ethical issues identified by interviewees were notably interdisciplinary in nature and invited questions regarding how to align priorities and values across disciplines and ensure clinical value throughout the development and implementation of medical AI and ML. Relatedly, interviewees suggested interdisciplinary solutions to these issues, for example, more resources to support knowledge generation and collaboration between developers and physicians, engagement with a broader range of stakeholders, and efforts to increase diversity in research broadly and within individual teams.
CONCLUSIONS: These qualitative findings help elucidate several ethical challenges anticipated or encountered in AI and ML for health care. Our study is unique in that its use of open-ended questions allowed interviewees to explore their sentiments and perspectives without overreliance on implicit assumptions about what AI and ML currently are or are not. This analysis, however, does not include the perspectives of other relevant stakeholder groups, such as patients, ethicists, industry researchers or representatives, or other health care professionals beyond physicians. Additional qualitative and quantitative research is needed to reproduce and build on these findings.

BACKGROUND: Telemedicine has the potential to remove geographic and temporal obstacles to health care access. Whether and how telemedicine can increase health care access for underserved populations remains an open question. To address this issue, we integrated facilitated telemedicine encounters for the management of hepatitis C virus (HCV), a highly prevalent condition among people with opioid use disorder (OUD), into opioid treatment programs (OTPs). In New York State, OTPs are methadone-dispensing centers that provide patient-centered, evidence-based treatment for OUD. We investigated the integration and impact of facilitated telemedicine into OTP workflows in these settings.
OBJECTIVE: This study aims to understand OTP staff experiences with integrating facilitated telemedicine for HCV treatment into OTPs, including best practices and lessons learned.
METHODS: We conducted semistructured interviews with 45 OTP staff members (13 clinical, 12 administrative, 6 physicians, and 14 support staff members) at least one year after the implementation of facilitated telemedicine for HCV management. We used hermeneutic phenomenological analysis to understand OTP staff experiences.
RESULTS: We identified 4 overarching themes illustrating the successful integration of facilitated telemedicine for HCV care into OTPs. First, integration requires an understanding of the challenges, goals, and values of the OTP. As OTP staff learned about new, highly effective HCV therapies, they valued an HCV cure as a \"win\" for their patients and were excited about the potential to eliminate a highly prevalent infectious disease. Second, the integration of facilitated telemedicine into OTPs fosters social support and reinforces relationships between patients and OTP staff. OTP staff appreciated the ability to have \"eyes on\" patients during telemedicine encounters to assess body language, a necessary component of OUD management. Third, participants described high levels of interprofessional collaboration as a care team that included the blurring of lines between disciplines working toward a common goal of improving patient care. Study case managers were integrated into OTP workflows and established communication channels to improve patient outcomes. Fourth, administrators endorsed the sustained and future expansion of facilitated telemedicine to address comorbidities.
CONCLUSIONS: OTP staff were highly enthusiastic about facilitated telemedicine for an underserved population. They described high levels of collaboration and integration comparable to relevant integrative frameworks. When situated within OTPs, facilitated telemedicine is a high-value application of telemedicine that provides support for underserved populations necessary for high-quality health care. These experiences support sustaining and scaling facilitated telemedicine in comparable settings and evaluating its ability to address other comorbidities.
BACKGROUND: ClinicalTrials.gov NCT02933970; https://clinicaltrials.gov/study/NCT02933970.

Biological visual systems rely on pose estimation of 3D objects to navigate and interact with their environment, but the neural mechanisms and computations for inferring 3D poses from 2D retinal images are only partially understood, especially where stereo information is missing. We previously presented evidence that humans infer the poses of 3D objects lying centered on the ground by using the geometrical back-transform from retinal images to viewer-centered world coordinates. This model explained the almost veridical estimation of poses in real scenes and the illusory rotation of poses in obliquely viewed pictures, which includes the \"pointing out of the picture\" phenomenon. Here we test this model for more varied configurations and find that it needs to be augmented. Five observers estimated poses of sloped, elevated, or off-center 3D sticks in each of 16 different poses displayed on a monitor in frontal and oblique views. Pose estimates in scenes and pictures showed remarkable accuracy and agreement between observers, but with a systematic fronto-parallel bias for oblique poses similar to the ground condition. The retinal projection of the pose of an object sloped wrt the ground depends on the slope. We show that observers\' estimates can be explained by the back-transform derived for close to the correct slope. The back-transform explanation also applies to obliquely viewed pictures and to off-center objects and elevated objects, making it more likely that observers use internalized perspective geometry to make 3D pose inferences while actively incorporating inferences about other aspects of object placement.

BACKGROUND: Mental health conditions are a significant public health problem globally, responsible for >8 million deaths per year. In addition, they lead to lost productivity, exacerbate physical illness, and are associated with stigma and human rights violations. Uganda, like many low- and middle-income countries, faces a massive treatment gap for mental health conditions, and numerous sociocultural challenges exacerbate the burden of mental health conditions.
OBJECTIVE: This study aims to describe the development and formative evaluation of a digital health intervention for improving access to mental health care in Uganda.
METHODS: This qualitative study used user-centered design and design science research principles. Stakeholders, including patients, caregivers, mental health care providers, and implementation experts (N=65), participated in focus group discussions in which we explored participants\' experience of mental illness and mental health care, experience with digital interventions, and opinions about a proposed digital mental health service. Data were analyzed using the Consolidated Framework for Implementation Research to derive requirements for the digital solution, which was iteratively cocreated with users and piloted.
RESULTS: Several challenges were identified, including a severe shortage of mental health facilities, unmet mental health information needs, heavy burden of caregiving, financial challenges, stigma, and negative beliefs related to mental health. Participants\' enthusiasm about digital solutions as a feasible, acceptable, and convenient method for accessing mental health services was also revealed, along with recommendations to make the service user-friendly, affordable, and available 24×7 and to ensure anonymity. A hospital call center service was developed to provide mental health information and advice in 2 languages through interactive voice response and live calls with health care professionals and peer support workers (recovering patients). In the 4 months after launch, 456 calls, from 236 unique numbers, were made to the system, of which 99 (21.7%) calls went to voicemails (out-of-office hours). Of the remaining 357 calls, 80 (22.4%) calls stopped at the interactive voice response, 231 (64.7%) calls were answered by call agents, and 22 (6.2%) calls were not answered. User feedback was positive, with callers appreciating the inclusion of peer support workers who share their recovery journeys. However, some participant recommendations (eg, adding video call options) or individualized needs (eg, prescriptions) could not be accommodated due to resource limitations or technical feasibility.
CONCLUSIONS: This study demonstrates a systematic and theory-driven approach to developing contextually appropriate digital solutions for improving mental health care in Uganda and similar contexts. The positive reception of the implemented service underscores its potential impact. Future research should address the identified limitations and evaluate clinical outcomes of long-term adoption.