BACKGROUND: Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes.
OBJECTIVE: This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA.
METHODS: We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases-PubMed, Embase, and IEEE Xplore-and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation).
RESULTS: A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence.
CONCLUSIONS: Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI\'s impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.

Ligustrum lucidum W.T. Aiton is an outstanding herb with the homology of medicine and food. Its ripe fruits are traditionally used as an important tonic for kidneys and liver in China. Ligustrum lucidum W.T. Aiton is rich in nutritional components and a variety of bioactive ingredients. A total of 206 compounds have been isolated and identified, they mainly include flavonoids, phenylpropanoids, iridoid glycosides, and triterpenoids. These compounds exert anti-osteoporosis, anti-tumor, liver protective, antioxidant, anti-inflammatory, and immunomodulatory effects. Ligustrum lucidum W.T. Aiton has been traditionally used to treat many complex diseases, including osteoporotic bone pain, rheumatic bone, cancer, related aging symptoms, and so on. In the 2020 Edition of Chinese Pharmacopoeia, there are more than 100 prescriptions containing L. lucidum W.T. Aiton. Among them, some classical preparations including Er Zhi Wan and Zhenqi fuzheng formula, are used in the treatment of various cancers with good therapeutic effects. Additionally, L. lucidum W.T. Aiton has also many excellent applications for functional food, ornamental plants, bioindicator of air pollution, algicidal agents, and feed additives. Ligustrum lucidum W.T. Aiton has rich plant resources. However, the application potential of it has not been fully exploited. We hope that this paper provides a theoretical basis for the high-value and high-connotation development of L. lucidum W.T. Aiton in the future.

Point-of-care ultrasound (POCUS) integration into neonatology offers transformative potential for diagnostics and treatment, enhancing immediacy and precision of clinical decision-making in this vulnerable patient population. This systematic review aims to synthesize evidence on POCUS applications, benefits, challenges, and educational strategies in neonatology. Literature search was conducted using SPIDER scheme keywords and MeSH terms related to POCUS and neonatology. Studies focusing on POCUS applications, its impact on clinical outcomes, and educational interventions for skill acquisition were included and analyzed using standardized tools, followed by a narrative synthesis of the findings. The search yielded 68 relevant publications, encompassing original research, reviews, and guidelines. POCUS applications varied across cardiovascular, pulmonary, neurological, and abdominal assessments. Key benefits included a reduced need for invasive procedures and rapid bedside diagnosis. Challenges included steep learning curves for clinicians and the need for standardized training and guidelines. Educational strategies highlighted the effectiveness of simulation-based training in enhancing ultrasound proficiency among neonatal care providers. POCUS represents a significant advancement in neonatal medicine, offering benefits for patient care. Addressing identified challenges through comprehensive training programs and developing standardized guidelines is crucial for optimized use. Future research should focus on evaluating educational outcomes and long-term impacts of POCUS integration into neonatal care.

BACKGROUND: The potential of artificial intelligence (AI) chatbots, particularly ChatGPT with GPT-4 (OpenAI), in assisting with medical diagnosis is an emerging research area. However, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in differential diagnosis lists.
OBJECTIVE: This study aims to assess the capability of GPT-4 in identifying the final diagnosis from differential-diagnosis lists and to compare its performance with that of physicians for case report series.
METHODS: We used a database of differential-diagnosis lists from case reports in the American Journal of Case Reports, corresponding to final diagnoses. These lists were generated by 3 AI systems: GPT-4, Google Bard (currently Google Gemini), and Large Language Models by Meta AI 2 (LLaMA2). The primary outcome was focused on whether GPT-4\'s evaluations identified the final diagnosis within these lists. None of these AIs received additional medical training or reinforcement. For comparison, 2 independent physicians also evaluated the lists, with any inconsistencies resolved by another physician.
RESULTS: The 3 AIs generated a total of 1176 differential diagnosis lists from 392 case descriptions. GPT-4\'s evaluations concurred with those of the physicians in 966 out of 1176 lists (82.1%). The Cohen κ coefficient was 0.63 (95% CI 0.56-0.69), indicating a fair to good agreement between GPT-4 and the physicians\' evaluations.
CONCLUSIONS: GPT-4 demonstrated a fair to good agreement in identifying the final diagnosis from differential-diagnosis lists, comparable to physicians for case report series. Its ability to compare differential diagnosis lists with final diagnoses suggests its potential to aid clinical decision-making support through diagnostic feedback. While GPT-4 showed a fair to good agreement for evaluation, its application in real-world scenarios and further validation in diverse clinical environments are essential to fully understand its utility in the diagnostic process.

The ever-evolving landscape of digital health technology has dramatically enhanced patients\' ability to manage their health through self-care effectively. These advancements have created various categories of self-care products, including medication management, health tracking, and wellness. There is no published research regarding integrating digital health into pharmacy self-care courses. This study aims to identify pertinent digital health devices and applications to incorporate into self-care course education. Digital health limitations, challenges incorporating digital health in self-care pharmacy education, and potential solutions are also reviewed. In conducting this research, many resources, including PubMed, APhA, ASHP, fda.gov, and digital.health, were reviewed in March 2024 to gather information on digital health devices and applications. To supplement this, targeted keyword searches were conducted on topics such as \"digital health\", \"devices\", \"applications\", \"technology\", and \"self-care\" across various online platforms. We identified digital health devices and applications suitable for self-care education across eight topics, as follows: screening, insomnia, reproductive disorders, eye disorders, home medical equipment, GI disorders, pediatrics, and respiratory disorders. Among these topics, wellness screening had the most digital health products available. For all other topics, at least three or more products were identified as relevant to self-care curriculum. By equipping students with digital health knowledge, they can effectively apply it in patient care throughout their rotations and future practice. Many digital health products, including telemedicine, electronic health records, mobile health applications, and wearable devices, are ideal for inclusion in pharmacy curriculum as future educational material. Future research is needed to develop the best strategies for incorporating relevant digital health into self-care education and defining the best student-learning strategies.

BACKGROUND: The COVID-19 pandemic created unprecedented challenges for people with type 2 diabetes (T2D) and prediabetes to access in-person health care support. Primary care teams accelerated plans to implement digital health technologies (DHTs), such as remote consultations and digital self-management. There is limited evidence about whether there were inequalities in how people with T2D and prediabetes adjusted to these changes.
OBJECTIVE: This study aimed to explore how people with T2D and prediabetes adapted to the reduction in in-person health support and the increased provision of support through DHTs during the COVID-19 pandemic and beyond.
METHODS: A purposive sample of people with T2D and prediabetes was recruited by text message from primary care practices that served low-income areas. Semistructured interviews were conducted by phone or video call, and data were analyzed thematically using a hybrid inductive and deductive approach.
RESULTS: A diverse sample of 30 participants was interviewed. There was a feeling that primary care had become harder to access. Participants responded to the challenge of accessing support by rationing or delaying seeking support or by proactively requesting appointments. Barriers to accessing health care support were associated with issues with using the total triage system, a passive interaction style with health care services, or being diagnosed with prediabetes at the beginning of the pandemic. Some participants were able to adapt to the increased delivery of support through DHTs. Others had lower capacity to use DHTs, which was caused by lower digital skills, fewer financial resources, and a lack of support to use the tools.
CONCLUSIONS: Inequalities in motivation, opportunity, and capacity to engage in health services and DHTs lead to unequal possibilities for people with T2D and prediabetes to self-care and receive care during the COVID-19 pandemic. These issues can be addressed by proactive arrangement of regular checkups by primary care services and improving capacity for people with lower digital skills to engage with DHTs.

Nanocomposites are sophisticated materials that incorporate nanostructures into matrix materials, such as polymers, ceramics and metals. Generally, the marine ecosystem exhibits severe variability in terms of light, temperature, pressure, and nutrient status, forcing the marine organisms to develop variable, complex and unique chemical structures to boost their competitiveness and chances of survival. Polymers sourced from marine creatures, such as chitin, chitosan, alginate, sugars, proteins, and collagen play a crucial role in the bioengineering field, contributing significantly to the development of nanostructures like nanoparticles, nanocomposites, nanotubes, quantum dots, etc. These nanostructures offer a wide array of features involving mechanical strength, thermal stability, electrical conductivity, barrier and optical characteristics compared to traditional composites. Notably, marine nanocomposites have distinctive roles in a wide spectrum of applications, among them anti-cancer, anti-microbial, antioxidant, cytotoxic, food packing, tissue engineering and catalytic actions. Sol-gel, hot pressing, chemical vapor deposition, catalytic decomposition, dispersion, melt intercalation, in situ intercalative polymerization, high-energy ball milling and template synthesis are common processes utilized in engineering nanocomposites. According to our literature survey and the Web of Science, chitosan, followed by cellulose, chitin and MAPs emerge as the most significant marine polymers utilized in the construction of nanocomposites. Taken together, the current manuscript underscores the biogenesis of nanocomposites, employing marine polymers using eco-friendly processes. Furthermore, significant emphasis in this area is needed to fully explore their capabilities and potential benefits. To the best of our knowledge, this manuscript stands as the first comprehensive review that discusses the role of marine-derived polymers in engineering nanocomposites for various applications.

BACKGROUND: Nurse burnout leads to an increase in turnover, which is a serious problem in the health care system. Although there is ample evidence of nurse burnout, interventions developed in previous studies were general and did not consider specific burnout dimensions and individual characteristics.
OBJECTIVE: The objectives of this study were to develop and optimize the first tailored mobile intervention for nurse burnout, which recommends programs based on artificial intelligence (AI) algorithms, and to test its usability, effectiveness, and satisfaction.
METHODS: In this study, an AI-based mobile intervention, Nurse Healing Space, was developed to provide tailored programs for nurse burnout. The 4-week program included mindfulness meditation, laughter therapy, storytelling, reflective writing, and acceptance and commitment therapy. The AI algorithm recommended one of these programs to participants by calculating similarity through a pretest consisting of participants\' demographics, research variables, and burnout dimension scores measured with the Copenhagen Burnout Inventory. After completing a 4-week program, burnout, job stress, stress response using the Stress Response Inventory Modified Form, the usability of the app, coping strategy by the coping strategy indicator, and program satisfaction (1: very dissatisfied; 5: very satisfied) were measured. The AI recognized the recommended program as effective if the user\'s burnout score reduced after the 2-week program and updated the algorithm accordingly. After a pilot test (n=10), AI optimization was performed (n=300). A paired 2-tailed t test, ANOVA, and the Spearman correlation were used to test the effect of the intervention and algorithm optimization.
RESULTS: Nurse Healing Space was implemented as a mobile app equipped with a system that recommended 1 program out of 4 based on similarity between users through AI. The AI algorithm worked well in matching the program recommended to participants who were most similar using valid data. Users were satisfied with the convenience and visual quality but were dissatisfied with the absence of notifications and inability to customize the program. The overall usability score of the app was 3.4 out of 5 points. Nurses\' burnout scores decreased significantly after the completion of the first 2-week program (t=7.012; P<.001) and reduced further after the second 2-week program (t=2.811; P=.01). After completing the Nurse Healing Space program, job stress (t=6.765; P<.001) and stress responses (t=5.864; P<.001) decreased significantly. During the second 2-week program, the burnout level reduced in the order of participation (r=-0.138; P=.04). User satisfaction increased for both the first (F=3.493; P=.03) and second programs (F=3.911; P=.02).
CONCLUSIONS: This program effectively reduced burnout, job stress, and stress responses. Nurse managers were able to prevent nurses from resigning and maintain the quality of medical services using this AI-based program to provide tailored interventions for nurse burnout. Thus, this app could improve qualitative health care, increase employee satisfaction, reduce costs, and ultimately improve the efficiency of the health care system.

Radiotherapy exhibits significant versatility and efficacy in cancer treatment, thereby playing a crucial role in the field of oncology. However, there remains an urgent need for extensive research on various aspects of radiotherapy, including target selection, damage repair and its combination with immunotherapy. Particularly, the development of in vitro models to replicate in vivo tumor lesion responses is vital. The present study provides a thorough review of the establishment and application of tumor organoids in radiotherapy, aiming to explore their potential impact on cancer treatment.

Du Zhong is a valuable Chinese medicinal herb unique to China. It is a national second- class precious protected tree, known as \"plant gold\", which has been used to treat various diseases since ancient times. The main active ingredients are lignans, phenylprophetons, flavonoids, iridoids and steroids and terpenoids, which have pharmacological effects such as lowering blood pressure, enhancing immunity, regulating bone metabolism, protecting nerve cells, protecting liver and gallbladder and regulating blood lipids. In this paper, a comprehensive review of Eucommia ulmoides Oliv. was summarized from the processing and its compositional changes, applications, chemical components, pharmacological effects, and pharmacokinetics, and the Q-marker of Eucommia ulmoides Oliv. is preliminarily predicted from the aspects of traditional efficacy, medicinal properties and measurability of chemical composition, and the pharmacodynamic substance basis and potential Q-marker of Eucommia ulmoides Oliv. are further analyzed through network pharmacology. It is speculated that quercetin, kaempferol, β-sitosterol, chlorogenic acid and pinoresinol diglucoside components are selected as quality markers of Eucommia ulmoides Oliv., which provide a basis for the quality control evaluation and follow-up research and development of Eucommia ulmoides Oliv.