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Latest Submissions Open for Peer Review

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JMIR Submissions under Open Peer Review

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Titles/Abstracts of Articles Currently Open for Review:

  • Background: Objectives: To explore the influencing factors and path of health information avoidance behavior of cancer patients in the age of smart media, and to construct a theoretical model of influencing factors. Methods: Face-to-face interviews were used to collect primary data, following the steps of the rooted theory research method. NVivo 12 software was applied to code and analyze the data, and a theoretical model of the factors influencing the health information avoidance behaviors of cancer patients was constructed by combining with the Stimulus-Human Body-Response (SOR) theory. Results: This study proposed six research hypotheses by analyzing the content of the interviews, which showed a causal relationship between psychological factors and health information avoidance behavior; personal, informational, and environmental factors, while indirectly influencing the health information avoidance behavior of cancer patients through the mediating role of psychological factors; capacity factors moderated the chain of factors from information factors and environmental factors to psychological factors to health information avoidance behaviors, respectively. Conclusions: In this study, we proposed a theoretical model of the factors influencing cancer patients' health information avoidance behavior in the smart media era. This model can summarize the influencing factors of cancer patients' occurrence of health information avoidance behaviors in the environment of the Smart Media Era, and provides research hypotheses and theoretical frameworks for further explaining the role path relationships between the influencing factors.

  • Background: With the rapid development and iteration of generative artificial intelligence, the growing popularity of such groundbreaking tools among nurse researchers, represented by ChatGPT, is receiving passionate debate and intrigue. Although there has been qualitative research on generative artificial intelligence in other fields, little is known about the experiences and perceptions of nurse researchers, and this study seeks to report on the subject. Objective: This study aimed to describe the experiences and perceptions of generative artificial intelligence among Chinese nurse researchers. Provide a reference for the application of generative artificial intelligence in nursing research in the future. Methods: Semi-structured interviews were used to collect data in this qualitative study. Data were analyzed employing inductive content analysis. Results: Five themes and twelve sub-themes were categorized from 27 original interview documents as follows: (1) Diverse reflections on human-machine symbiosis, which includes the interplay between substitution, researchers shaping the potential space of generative artificial intelligence, and researchers accepting generative artificial intelligence with alacrity; (2) Heterogeneity of groups and experiences, including diversity in experiences of using and heterogeneity in the perception and use among different groups; (3) Research paradigm reshaping in the infancy stage, which involves a groundbreaking auxiliary tool in nursing research and the incubation of innovative research paths; (4) Ethical concerns and application challenges, considering insight into the public opinion around generative artificial intelligence, academic integrity and medical ethical challenges, and limitations on application in nursing research; (5) Future development and capacity reinforcement, which concerns reinforcement needs for utilization competency and collaboration and exploration in future nursing research. In this context, the first four themes form the rocket of the human-machine symbiosis journey. Only when humans fully leverage the advantages of machines (generative artificial intelligence) and overcome the shortcomings of them, can this human-machine symbiosis journey reach towards the correct future direction (fifth theme). Conclusions: This study explored the experiences and perceptions of nurse researchers interacting with generative artificial intelligence, which was a "symbiotic journey" full of windings. The human-machine interaction process relentlessly moves nurse researchers to improve scientific literacy, digital literacy, and prompt skills. Meanwhile, the potential hazards and concerns of this topic for nurse researchers became apparent, with an emphasis on academic integrity, drafting relevant specifications, and the accuracy of generated content. Collaboration with interdisciplinary professionals, utilizing supervised fine-tuning, knowledge graphs, and retrieval augmented generation techniques, to develop nursing research-specific multimodal artificial general intelligence was expected to meet the individual needs of nurse researchers.

  • Background: Crohn's disease (CD), a complex member of the inflammatory bowel disease spectrum, is characterized by the diversity and skipping distribution of intestinal mucosal lesions, significantly complicating its differential diagnosis with intestinal diseases such as ulcerative colitis and intestinal tuberculosis. With the increasing application of artificial intelligence (AI) in the medical field, its utilization in clinical diagnosis has become more widespread. Objective: However, there is a lack of systematic evaluation regarding the specific efficacy of AI in identifying CD through capsule endoscopy. Methods: This study conducted a comprehensive search of PubMed databases, Cochrane, EMBASE, and Web of Science up to May 21, 2024, to collect relevant literature. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to rigorously assess the quality of included studies, and detailed information on study characteristics and AI algorithms was extracted. A bivariate mixed-effects model was employed to synthesize and analyze the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, meta-regression and subgroup analyses were conducted to delve into the potential sources of heterogeneity. Results: Ultimately, eight studies encompassing 11 distinct AI models were included in this meta-analysis. The overall area under the curve (AUC) for AI in identifying CD through capsule endoscopy (CE) was 99% (95% CI, 100%-0.00), indicating high diagnostic accuracy. Specifically, the pooled sensitivity was 94% (95% CI, 93%-96%), specificity was 97% (95% CI, 95%-98%), positive likelihood ratio (PLR) was 32.7 (95% CI, 19.9-53.6), negative likelihood ratio (NLR) was 6% (95% CI, 4%-7%), and diagnostic odds ratio (DOR) reached 576 (95% CI, 295-1127). Meta-regression analysis further revealed that AI algorithm type, study population size, and study design might be key sources of heterogeneity. Conclusions: This study demonstrates the significant potential of AI technology in assisting endoscopists in detecting and identifying CD patients through capsule endoscopy. However, given the limitations and heterogeneity of current research, more high-quality, large-sample studies are needed to comprehensively and thoroughly evaluate the practical application value of AI in CD diagnosis, thereby promoting its widespread adoption and optimization in clinical practice.

  • Background: Acute pancreatitis (AP) is one of the most prevalent gastrointestinal diseases in clinical practice. In addition to essential medication therapy, a nutritional diet also plays a vital part in the treatment. People are increasingly using online short video platforms to look up health-related information with the widespread use of smartphones. However, the quality and reliability of health content on these platforms remain unknown. Objective: This study aimed to assess the quality and reliability of the information in AP diet–related videos on Chinese short-video-sharing platforms. Methods: A total of 147 videos were included to analyze from three of the most widely used short-video sharing platforms in China, TikTok, BiliBili, and WeChat channels. Each video was assessed by two physicians separately for content (by content score), quality (by Global Quality Score), and reliability (by an adjusted DISCERN tool). Poisson regression and correlation analysis were used to explore the variables that might affect the quality of the video. Results: videos from TikTok had the most likes and comments than videos from TikTok and WeChat channels, and videos from BiliBili were longer in duration and in days since published than other videos (all p<.001). However, there was no significant difference in the GQS, content score and the DISCERN score among videos from TikTok, BiliBili, and WeChat channels (p>.05). The overall quality of the videos was poor. videos from medical professionals had a relatively greater advice value than those from non-medical professionals in the field of content trustworthiness, quality, and comprehensiveness. The subsequent variables were correlated positively: likes and shares (r=0.326, p<.001), likes and comments (r=0.439, p<.001), comments and shares (r=0.337, p<0.001). DISCERN scores and days since published were found to be negatively correlated (r=-0.259, p<.001). Conclusions: The findings showed that these videos’ quality was inadequate and varied greatly based on the kind of source. In general, videos uploaded by medical professionals were proved to be more reliable, comprehensive, and high-quality than non-medical professionals' videos in content quality. these platforms were not a suitable source of information for patient education. But given the rise in popularity of video-sharing platforms, necessary regulations and restrictions should be taken.

  • Background: Depressive disorders, projected by the WHO to rank third in global disease burden by 2030, significantly impair quality of life, with over 300 million affected worldwide. Sleep difficulties, particularly insomnia, are closely linked to depression, exacerbating its severity. Biofeedback (BF) therapy, specifically heart rate variability biofeedback (HRV-BF), shows promise in treating insomnia and associated depressive symptoms. Recent studies suggest that integrating virtual reality (VR) into BF therapy could enhance its efficacy by creating immersive, calming environments that improve sleep quality. This study explores the potential of VR-based BF to improve sleep in individuals with depression and anxiety. Objective: To investigate impact of virtual reality-based biofeedback (VR-based BF) on sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI) of individuals with depressive or anxiety symptoms. Methods: Between December 2019 and February 2022, 131 adult volunteers were recruited at Samsung Medical Center, Seoul, South Korea. Individuals with specific medical or psychiatric conditions were excluded. Those with depressive and anxiety symptoms (DAS) were randomized into VR (n = 40) and BF (n = 38) groups by computer-generated random number. A Healthy Control (HC, n = 40) cohort with sham intervention mirroring the DAS/VR group was also included. Over three visits, participants received VR-based BF or conventional BF with a therapist. Iterative baseline and 4-week follow-up PSQI assessments were performed. Following intervention, subcomponent scores of the PSQI decreased in both DAS/VR and DAS/BF groups. Results: After 4-week intervention, a decrease in global PSQI scores was observed across all groups. For Global PSQI score, the DAS/VR group demonstrated a substantial reduction from 9.70 (±2.49) to 7.20 (±2.46) (p<0.001), and the HC/VR group from 5.85 (±2.39) to 4.90 (±2.11) (p=0.007). Notably, improvements in sleep quality, latency, disturbance, and day dysfunction were statistically significant in the DAS/VR groups but not in sleep duration, efficiency, and sleep medicine uses. Conclusions: This study provides evidence that VR-based BF can serve as effective psychological intervention for enhancing sleep quality of individuals experiencing symptoms of depression and anxiety, and also effective in health subjects.

  • Older Adults' Preferences for Caregiving AI Chatbots to Improve Well-being and Social Connectivity

    Date Submitted: Aug 25, 2024
    Open Peer Review Period: Aug 26, 2024 - Oct 21, 2024

    Background: The increasing number of older adults who are living alone poses challenges for maintaining their well-being, as they often need support with daily tasks, healthcare services, and social connections. However, advancements in artificial intelligence (AI) technologies have revolutionized healthcare and caregiving via their capacity to monitor health, provide medication and appointment reminders, and companionship to older adults. Nevertheless, the adaptability of these technologies for older adults are stymied by useability issues. This study explores how older adults use and adapt to AI technologies, highlighting both the persistent barriers and opportunities for potential enhancements. Objective: The study purpose was to provide deeper insights into older adults' engagement with technology and AI. The technologies currently used, potential technologies desired for daily life integration, personal technology concerns faced, and overall attitudes towards technology and AI are explored. Methods: Using mixed-methods, participants (N = 28) completed both a semi-structured interview and surveys consisting of health and well-being measures. Participants then participated in a research team facilitated interaction with an AI chatbot, Amazon Alexa. Interview transcripts were analyzed using thematic analysis, and surveys were evaluated using descriptive statistics. Results: Participants ranged in age from 65 to 84 years. Digital devices were most commonly used for entertainment, health management, professional productivity, and social connectivity. Participants were most interested in integrating technology in their personal life for scheduling reminders, chore assistance, and for providing care to others. Challenges in using new technology included commitment to learning, a lack of privacy, and a worry about future technology dependence. Overall, their attitudes coalesced towards early adapters, those wary, and those who were resisters of technology and AI. Conclusions: To ensure that AI technologies effectively support older adults, it's essential to foster ongoing dialogue among developers, older adults, families, and their caregivers, focusing on inclusive designs to meet older adults’ needs.

  • A bibliometric analysis of the advance of artificial intelligence in medicine

    Date Submitted: Aug 25, 2024
    Open Peer Review Period: Aug 26, 2024 - Oct 21, 2024

    Background: The integration of artificial intelligence (AI) into medicine has ushered an era of unprecedented innovation, with substantial impacts on healthcare delivery and patient outcomes. Objective: it is essential to comprehend the current state of development, primary research focal points, and to identify key contributors and their relationships in the application of AI in medicine through bibliometric analysis. Methods: We employed the Web of Science Core Collection as our primary database and conducted a literature search spanning from January 2019 to December 2023.VOSviewer and R-bibliometrix were performed to conduct bibliometric analysis and network visualization, including the number of publications, countries, journals, citations, authors and keywords. Results: A total of 1811 publications on research for artificial intelligence in medicine were released across 565 journals by 12376 authors affiliated with 3583 institutions from 97 countries. The United States emerged as the leading producer of scholarly works, exerting significant influence in this domain. Harvard Medical School exhibited the highest publication count among all institutions. The JOURNAL OF MEDICAL INTERNET RESEARCH attained the highest H-index (H-index=19), the most significant publication count (NP=76), and total citations (NC=1495). Among the keywords, four clusters were identified, encompassing the application of AI in digital health, COVID-19 and ChatGPT, precision medicine, epidemiology, and public health. "Outcomes" and "Risk" demonstrated a notable upward trend, indicating the utilization of AI in engaging with clinicians and patients to discuss patients' health condition risks, foreshadowing future research focal points. Conclusions: Our bibliometric analysis delved into the advancements, focal points, and cutting-edge areas within the field of artificial intelligence in medicine, revealing potential future research opportunities. Research on artificial intelligence in medicine is rapidly progressing, as evidenced by a consistent increase in publications on the topic since 2019. Simultaneously, we identified leading countries, institutions, and scholars in the field and conducted an analysis of journals and representative literature. This study equips researchers with the necessary information to comprehend the current state, collaborative networks, and primary research focal points within the field. Furthermore, our findings propose a set of recommendations for future research.

  • Background: Health and social care models worldwide are facing perpetual crisis where the informal (family) caring role is becoming increasingly pivotal. Despite unparalleled societal and economic value, many informal carers face poor mental and physical health with limited opportunities for physical activity. There remains an urgent need to understand and support informal carers to stay well, including evidence based physical activity approaches. Objective: To codesign, adapt and explore the feasibility of a novel cross-platform approach to support physical activity in carers of people with dementia. Methods: This was a mixed-methods codesign, development and evaluation study of a smartphone app (CareFit) to support physical activity for unpaid dementia carers. We explored implementation of CareFit for carers, guided by both ‘RE-AIM’ and MRC Complex Intervention Frameworks in two stages: (i) codesign; (ii) feasibility study findings (i.e. recruitment, intervention and outcome selection). The codesign sessions for adaptation and expansion involved 3 development sprints gaining feedback and identifying priority areas from a range of stakeholders (e.g. carers, support professionals, charities, researchers and developers). This was followed by an 8-week feasibility study with participants recruited from local and national networks alongside Join Dementia Research (JDR) using a closed-testing release app on Google and Apple app stores. Results: We successfully codesigned, developed and user tested the CareFit app. Codesign resulted in an expanded and adapted CareFit suitable for 8-weeks of use. Final app design included a simplified navigation system, increased video content alongside more personalised delivery of content. Feasibility study results highlighted the challenges of recruiting carers of people with dementia. In total 41 carers of people with dementia were recruited with 21 completing the 8-week study. Study retention was considerably lower for those carers undertaking high levels of physical activity at baseline opposed to those who were not (36% retention vs 58% respectively) providing useful information on the target group of future interventions. CareFit rated well on the System Usability Scale and we observed common user patterns of behaviour (e.g. an initial focus on ‘learn’ section). Most outcome measures were largely suitable for future use in this group- this included novel measures introduced by the research team around the number of sedentary breakers and muscle and balance activities. Conclusions: Physical activity in carers of people with dementia remains a largely unmet need. We conclude that our approach fits largely within the context of preventative medicine where presentation to carers at the ‘right’ time in their trajectory (i.e. early) is critical for adoption and long-term use. A major challenge remains around recruitment. Despite value recognised by stakeholders including carers, we cannot currently recommend progression to randomised control trial. Of future interest would be to build upon this work further to accumulate evidence on optimising the active ingredients of the intervention.

  • Background: Dengue cases were reported at approximately 400 million cases worldwide, with 70% of the burden in Asia. Dengue constitutes a significant public health concern given its increasing prevalence and severe complications. Military camps are dengue hotspots, exposing the personnel to dengue infection and putting the military forces’ strength at risk due to the need to be hospitalized and long absences. Objective: To assess the effects of digital mobile of Information-Motivation-Behavioral Skills Dengue Intervention Module (IMODE) in increasing dengue preventive practices among heads of households in military quarters. Methods: This was a two-arm, single-blinded, cluster randomized control trial conducted in one of the army camps in Kuala Lumpur, Malaysia. The intervention comprised web-based and mobile digital information and field activities delivered for two weeks and reinforced over three months with dengue preventive practices as the primary outcome. The secondary outcomes included knowledge, motivation, and behavioral skills assessed using validated self-administered questionnaires at baseline, immediate, and three months post-intervention. The intervention’s effectiveness was evaluated using generalized estimating equations adjusted for covariates such as household income, rank number of children, and baseline data. Results: A total of 201 participants completed the study, with an attrition rate of 14.1% (33/234). At three months post-intervention, the intervention group demonstrated significantly increased knowledge (β=18.38, 95% CI 13.20 to 23.56; P<.001) and increased motivation (β=10.33, 95% CI 7.27 to 13.39; P < .001) compared to the control group. Even though dengue preventive behavioral skills increased in the intervention group compared to the control group (β=8.57, 95% CI 3.32 to 13.83; P = .001) but no significant difference was observed for dengue preventive practices (P = .08). Conclusions: IMODE offers the opportunity to improve knowledge, motivation, and dengue-preventive behavioral skills among military personnel. Nevertheless, a longer duration of the intervention is recommended to enhance behavioral change and dengue preventive practices among this dengue high-risk group. Clinical Trial: Thai Clinical Trial Registry TCTR20211021004. https://www.thaiclinicaltrials.org/show/ TCTR20211021004

  • Background: Ecological Momentary Assessment (EMA) is pivotal in longitudinal health research in youth, but potential bias associated with non-participation, omitted reports, or dropout threaten its clinical validity. Prior meta-analytic evidence is inconsistent regarding specific determinants of missing data. Objective: This meta-analysis aims to update and expand upon previous research by examining key participation metrics—acceptance, compliance, and retention—in youth EMA studies. Additionally, it seeks to identify potential moderators among sample and design characteristics, with the goal of better understanding and mitigating the impact of missing data. Methods: We revised the bibliographic database search to identify EMA studies involving children and adolescents published from 2001 to November 2023. Eligible studies utilized mobile-delivered EMA protocols in samples with average age up to 18 years. We conducted separate meta-analyses for acceptance, compliance, and retention rates, and performed meta-regressions to address sample and design characteristics. Furthermore, we extracted and pooled sample-level effect sizes related to correlates of response compliance. Risk of publication bias was assessed using funnel plots, regression tests, and sensitivity analyses targeting inflated compliance rates. Results: We identified 285 samples including 17,441 participants, aged 5 to 17.96 years (mean age 14.22, SD 2.24, mean %-female 55.7%). Pooled estimates were 67.27% (k=88, 95% CI 62.39 to 71.96) for acceptance, 71.97% (k=216, 95% CI 69.83 to 74.11) for compliance, and 96.57% (k=169, 95% CI 95.42 to 97.56) for retention. Despite overall poor moderation of participation metrics, acceptance rates decreased with an increase in EMA items (log-transformed, β = -0.115, SE = 0.036; 95% CI -0.185 to -0.045, P=.001, R2=19.98), compliance rates declined by 0.8% per year of publication (SE 0.25, 95% CI -1.3 to -0.3, P=.002, R2=4.17), and retention rates dropped with increasing study durations (log-transformed, β = -0.061,SE 0.015, 95% CI -0.091 to -0.091, P=<.001, R2=10.06). Benefits of monetary incentivization on response compliance attenuated with increasing proportion of female participants (β=-0.002, SE 0.001; 95% CI -0.003 to -0.001, P=.003, R2=9.47). Within-sample analyses showed a small but significant effect indicating higher compliance in girls compared to boys (k=25, g=0.18, 95% CI 0.06-0.31, P=.003), but no significant age-related effects were found (k=14, z score = 0.05, 95% CI -0.01 to 0.16). Conclusions: Despite a 5-fold increase in included effect sizes compared to the initial review, the variability in rates of missing data based one can expect based on specific sample and design characteristics remains substantial. The inconsistency in identifying robust moderators highlights the need for greater attention to missing data and its impact on study results. To eradicate any health-related bias in EMA studies, researchers should collectively increase transparent reporting practices, intensify primary methodological research, involve participants’ perspectives on missing data. Clinical Trial: PROSPERO CRD42022376948; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022376948

  • Background: Real-world data (RWD) from sources like administrative claims, electronic health records, and cancer registries offer insights into patient populations beyond the tightly regulated environment of randomized controlled trials. To leverage this and to advance cancer research, six university hospitals in Bavaria have established a joint research IT infrastructure. Objective: This article aims to outline the design, implementation, and deployment of a modular data transformation pipeline that transforms oncological RWD into HL7 (Health Level 7) FHIR (Fast Healthcare Interoperability Resources) format and then into a tabular format in preparation for a federated analysis (FA) across the six BZKF university hospitals. Methods: To harness RWD effectively, we designed a pipeline to convert the oncological basic dataset (oBDS) into HL7 FHIR format and prepare it for federated analysis. The pipeline handles diverse IT infrastructures and systems while maintaining privacy by keeping data decentralized for analysis. To assess the functionality and validity of our implementation, we defined a cohort to address two specific medical research questions. We evaluated our findings by comparing the results of the FA with reports from the Bavarian Cancer Registry and the original data from local tumor documentation systems. Results: We conducted a federated analysis of 17,885 cancer cases from 2021/2022. Breast cancer was the most common diagnosis at three sites, prostate cancer ranked in the top two at four sites, and malignant melanoma was notably prevalent. Gender-specific trends showed larynx and esophagus cancers were more common in males, while breast and thyroid cancers were more frequent in females. Discrepancies between the Bavarian Cancer Registry and our data, such as higher rates of malignant melanoma (5 % vs. 11 %) and lower representation of colorectal cancers (13 % vs. 7 %) likely result from differences in the time periods analyzed (2019 vs. 2021/2022) and the scope of data sources used. The Bavarian Cancer Registry reports approximately three times more cancer cases than the six university hospitals alone. Conclusions: The modular pipeline successfully transformed oncological RWD across six hospitals, and the federated approach preserved privacy while enabling comprehensive analysis. Future work will add support for recent oBDS versions, automate data quality checks, and integrate additional clinical data. Our findings highlight the potential of federated health data networks and lay the groundwork for future research that can leverage high-quality RWD, aiming to contribute valuable knowledge to the field of cancer research.

  • Background: The rapid shift to video consultation services during the COVID-19 pandemic has raised concerns about exacerbating existing health inequities, particularly for disadvantaged populations. Intersectionality theory provides a valuable framework for understanding how multiple dimensions of disadvantage interact to shape health experiences and outcomes. Objective: To explore how multiple dimensions of disadvantage - specifically older age, limited English proficiency, and low socioeconomic status - intersect to shape experiences with digital health services, focusing on video consultations. Methods: Guided by intersectionality theory and digital capital concepts, semi-structured narrative interviews were conducted with 17 participants aged 65 or older from diverse ethnic backgrounds in the Redbridge borough of London. Interviews explored participants' experiences accessing healthcare virtually. Intersectional narrative analysis was used to identify key themes and examine how different forms of disadvantage interact. Theoretically-informed narrative portraits and user personas were developed to synthesize findings. Results: Analysis revealed that digitalisation of healthcare can exacerbate existing inequities, erode trust, compound oppression, and reduce patient agency for multiply-disadvantaged patient populations. Examining intersectionality illuminated how age, language proficiency, and socioeconomic status interact to create unique barriers and experiences. Key themes included: weakened presence in digital interactions, erosion of therapeutic relationships, shift from relational to distributed continuity, increased complexity leading to disorientation, engagement shaped by prior experiences of discrimination, and reduced patient agency. Conclusions: This study provides critical insights into how the digitalisation of healthcare can deepen disparities for older, low-income, limited English speaking patients. By applying intersectionality theory to digital health disparities, our findings underscore the urgent need for multifaceted approaches to digital health equity that address the complex interplay of disadvantage. Recommendations include co-designing inclusive digital services, strengthening relational continuity, and developing targeted support to preserve agency and trust for marginalized groups in an increasingly digital healthcare landscape.

  • Background: Emotional support plays a crucial role in enhancing social interactions, facilitating psychological interventions, and improving customer service outcomes by addressing individuals' emotional needs. The emergence of large language models (LLMs) holds promise for delivering emotional support on a large scale, but their effectiveness compared to human counselors is still not well understood. Evaluating and enhancing the emotional support capabilities of LLMs through targeted user-centered strategies is crucial for their successful real-world integration. Objective: This study aims to evaluate the emotional support capabilities of large language models (LLMs), specifically GPT-4o, and to introduce an integrative automatic evaluation framework centered on user-perceived feedback. The framework is designed to enhance LLM performance in emotional support conversations (ESCs) by identifying psycholinguistic clues as intrinsic evaluation metrics and leveraging a customized Chain-of-Thought (CoT) prompting framework. Methods: The study used a dataset of emotional support conversations from human counselors. An explanatory predictive model was developed using explainable artificial intelligence methods, following an integrative modeling paradigm rooted in computational social science. The model evaluated and interpreted user-perceived feedback scores for GPT-4o. Additionally, the study integrated Hill’s three-stage model of helping into a manually customized chain of thought prompting framework to systematically evaluate GPT-4o's performance in ESCs. Results: GPT-4o achieved high user-perceived feedback scores, demonstrating relative stability in its performance, but it still significantly trails behind human counselors overall (Cliff's Delta = 0.087, p < 0.001). The evaluation framework, which identified 41 distinct linguistic clues related to emotional expression, social dynamics, cognitive processes, linguistic style, and decision-making stages, enhanced the understanding of both processes and outcomes in ESCs. Notably, GPT-4o's user-perceived feedback scores significantly improved with the use of manually customized Chain of Thought prompts (p < 0.001, Cohen's d: 0.378), but showing no significant difference from the average performance of human counselors overall (p-adj: 0.47, Cliff's Delta: -0.014). However, thought prompts demonstrate a significant advantage in specific emotion categories such as fear (p: 0.002, Cliff's Delta: -0.23), sadness (p: 0.012, Cliff's Delta: -0.105), and break up with partner issues (p: 0.254, Cliff's Delta: -0.06). However, GPT-4o exhibited weaknesses in emotional understanding, cognitive complexity, language fluency, and handling extreme scenarios. Conclusions: This study provides preliminary evidence of GPT-4o's emotional support capabilities and proposes a user-perceived feedback-centered integrative evaluation framework for ESCs. The findings suggest a cautiously optimistic outlook for the application of advanced large language models (LLMs) in emotional support services, although significant challenges remain, particularly in enhancing the depth of exploration in conversations and the personalization of language. The proposed framework encourages the integration of human expertise into LLMs, enhancing their efficacy and advancing the development of trustworthy AI-based emotional support services.

  • Background: The Automated Heart-Health Assessment (AH-HA) tool is a novel electronic health record clinical decision support tool based on the American Heart Association’s Life’s Simple 7 cardiovascular health (CVH) metrics to promote CVH assessment and discussion in outpatient oncology. Before proceeding to future implementation trials, it is critical to establish the acceptability of the tool among providers and survivors. Objective: We assessed provider and survivor acceptability of the AH-HA tool and provider training at practices randomized to the AH-HA tool arm within WF-1804CD. Methods: Providers (physicians, nurse practitioners, physician assistants) completed a survey to assess acceptability of the AH-HA training, immediately following training. Providers also completed surveys to assess AH-HA tool acceptability and potential sustainability. Tool acceptability was assessed after 30 patients were enrolled at the practice with both a survey developed for the study as well as with domains from the Unified Theory of Acceptance and Use of Technology (UTAUT) survey (Performance Expectancy, Effort Expectancy, Attitude Toward using Technology, and Facilitating Conditions). Semi-structured interviews at the end of the study captured additional provider perceptions of the AH-HA tool. Post-treatment survivors (breast, prostate, colorectal, endometrial, and lymphomas) completed a survey to assess acceptability of the AH-HA tool immediately after the designated study appointment. Results: Providers (n=15) reported high overall acceptability of the AH-HA training (mean=5.8, SD=1.0) and tool (mean=5.5, SD =1.4); provider acceptability was also supported by UTAUT scores (e.g., Effort Expectancy mean=5.6, SD=1.5). Qualitative data also supported provider acceptability of different aspects of the AH-HA tool (e.g., It helps focus the conversation and give the patient a visual of continuum of progress). Providers were more favorable about using the AH-HA tool for post-treatment survivorship care. Enrolled survivors (n=245) were an average of 4.4 years post-treatment (SD =3.7). Most survivors reported that they strongly agreed/agreed that they liked the AH-HA tool (94.3%, n=231). A larger proportion of survivors with high health literacy strongly agreed/agreed that it was helpful to see their heart health score (98.2%, n=161) compared to survivors with lower health literacy scores (89.5%, n=68; p=0.005). Conclusions: Quantitative surveys and qualitative interview data both demonstrate high acceptability of the AH-HA tool among both providers and survivors. Although most survivors found it helpful to see their heart health score, there may be room for improving communication with survivors who have lower health literacy. Clinical Trial: Assessing Effectiveness and Implementation of an EHR Tool to Assess Heart Health Among Survivors (AH-HA) NCT03935282 https://clinicaltrials.gov/study/NCT03935282?term=NCT03935282&rank=1

  • Background: In China, there is limited understanding of the complex interrelationships between information technology perception, value co-creation behavior, patient trust, and patient satisfaction with medical care among community-based chronic disease patients. Objective: This study aims to explore the relationships between information technology perception, value co-creation behavior, patient trust, and patient satisfaction with medical care among chronic disease patients in Chinese communities. Additionally, it investigates the mediating role of value co-creation behavior between information technology perception and patient satisfaction, as well as the moderating role of patient trust in the relationship between value co-creation behavior and patient satisfaction. Methods: A cross-sectional survey was conducted from September 2019 to December 2019 in Wuhan and Taiyuan, China. Participants were selected using a multistage stratified random sampling method. Data were collected via self-administered questionnaires from 722 chronic disease patients in Wuhan and Taiyuan (with a response rate of 90.36%). Patient satisfaction with medical care was measured using a four-item scale. Information technology perception was assessed using scales for perceived ease of use and perceived reliability, adapted from Deng Chaohua et al.'s measurement of mobile banking system perception. Value co-creation behavior was measured using a 21-item scale adapted from Yi and Gong's measurement of customer value co-creation behavior, and patient trust was measured using a four-item scale. Pearson correlation analysis was used to assess the relationships among perceived ease of use, perceived reliability, value co-creation behavior, patient trust, and patient satisfaction. Structural equation modeling (SEM) was employed to examine the hypothesized relationships among these variables. Results: The proposed model demonstrated good model fit. Perceived ease of use (β=0.339, P<.001) and value co-creation behavior (β=0.459, P<.001) had direct positive effects on patient satisfaction, while perceived reliability (β=0.049, P=.520) did not have a direct effect on patient satisfaction. Perceived ease of use (β=-.746, P<.001) had a direct negative effect on value co-creation behavior, whereas perceived reliability (β=0.408, P<.001) had a direct positive effect on value co-creation behavior. The relationship between perceived ease of use (β=-0.342, P<.001), perceived reliability (β=0.187, P<.001), and patient satisfaction was indirectly influenced by value co-creation behavior. The mediating role of value co-creation behavior in the relationships between perceived ease of use (β=-0.075, P<.001), perceived reliability (β=0.034, P<.001), and patient satisfaction was negatively moderated by patient trust. Conclusions: Our study elucidates the pathways linking perceived ease of use, perceived reliability, value co-creation behavior, patient trust, and patient satisfaction with medical care. The findings indicate that perceived ease of use and value co-creation behavior significantly influence patient satisfaction, while perceived ease of use and perceived reliability significantly impact value co-creation behavior. The influence of perceived ease of use and perceived reliability on patient satisfaction can be mediated by value co-creation behavior, and the impact of value co-creation behavior on patient satisfaction is moderated by patient trust.

  • Background: The prevalence of health misinformation on social media is a complex and pressing issue. Addressing this issue is a commendable pursuit, but its effectiveness is often hindered by the diverse ways in which individuals interpret and understand information. Objective: This study identified the attributes of correction posts and categories of user engagement by investigating (1) the trend of user engagement with health misinformation correction during three years of the COVID-19 pandemic (2020-2022); (2) the relationship between post attributes and user engagement in sharing and reactions; and (3) the content generated by user comments serving as additional information attached to the post, affecting user engagement in sharing and reactions. Methods: Data were collected from the Facebook pages of a fact-checking organization and a health agency from January 2020 to December 2022. A total of 1,424 posts and 67,905 corresponding comments were analyzed. The posts were manually annotated by developing a research framework based on the fuzzy trace theory, categorizing information into "gist" and "verbatim" representations. Three types of gist representations were examined: "risk: risks associated with misinformation," "awareness: awareness of misinformation," and "value: value in health promotion." Further, three types of verbatim representations were identified: "numeric: numeric and statistical bases for correction"; "authority: authority from experts, scholars, or institutions"; and "facts: facts with varying levels of detail." The basic indicators of user engagement included shares, reactions, and comments as the primary dependent variables. Moreover, this study examined user comments and classified engagement as cognitive (knowledge-based, critical, and bias-based) or emotional (positive, negative, and neutral). Statistical analyses were performed to explore the impact of post attributes on user engagement. Results: Over time, the number of shares and reactions decreased, while comments reached their highest point in the second year before dropping to the lowest level in the third year. Bias-based engagement comprised a larger portion of the discussion during the second and third years. Regression models examining the relationship between the attributes of correction posts and user engagement categories revealed results aligning with the theory, indicating that all three types of gist representations significantly predicted shares. Awareness also significantly predicted reactions and comments, demonstrating that emphasizing the gist enhanced user engagement. When incorporating the types of comments into the model, bias-based engagement negatively impacted shares. Conclusions: This study presents a practical framework for understanding and examining how people react to misinformation corrections. The findings could guide efforts to design effective messages to address health misinformation and foster a more knowledgeable online environment. These practical implications make this study highly relevant and applicable in the field of health communication.

  • Background: Families are often unsure how best to prepare dependent children for the death of a significant caregiver with a poor cancer prognosis, seeking guidance and support from their healthcare team. Health and social care professionals (professionals) often lack educational opportunities to gain the desired knowledge, skills, and confidence to provide family-centred supportive cancer care. To improve educational opportunities in healthcare, eLearning has positively impacted access and reach of effective evidence-based interventions. Objective: To evaluate the acceptability, useability, effectiveness and outome of an evidence-based and theory-driven eLearning intervention to equip and promote professionals’ self-efficacy to deliver family-centred supportive cancer care, when an adult with significant caregiving responsibilities for dependent children is at end of life. Methods: Guided by the ‘Person-based Approach’, a mixed-methods outcome evaluation was utilised. A validated pre-test post-test survy was used to determine self-efficacy and one-to-one remote semi-structured interviews, alongside single-item questions explore professionals’ experience and perceived learning onto clinical practice. To generate enhanced insights, the quantitative and qualitative data was integrated through a four stage modified pillar integration process. Results: One-hundred and fifty eight participants completed the pre-test survey prior to engaging with the eLearning resource, with a total of 99 participants completing the post-test survey. Semi-structured qualitative interviews were conducted with 12 professionals at least one month post-intervention. Findings highlighted a statistically significant improvement in professionals’ post-test self-efficacy (n = 99, p = <0.001). Professionals reported a preparedness to engage in supportive adult-professional end of life cancer care conversations, when an adult with significant caregiving responsibilities is dying. Findings demonstrated transferable learning to additional contexts such as other close adult-child relational bonds (grandparents) and to life-limiting conditions. Usability of the eLearning resource was reported positively, focusing on look and feel, length and integration of multimedia elements. Conclusions: The systematic and iterative “Person-based Approach’ optimised the acceptability of a novel eLearning intervention, having the potential to promote family-centred supportive end of life cancer care. This accessible eLearning intervention makes an important contribution to the recognised global gap of educational interventions in this field. Equipping professionals on family-centred supportive end of life care improves self-efficacy and preparedness to engage in challenging parent-professional communication, with the potential to promote better outcomes for affected adults and children and mediate for adverse outcomes for adults and children pre-and post-bereavement.

  • Data interoperability in the VACCELERATE project: why it matters and making it meaningful

    Date Submitted: Aug 21, 2024
    Open Peer Review Period: Aug 20, 2024 - Oct 15, 2024

    Background: Data standards are not only the key to making data processing efficient, but also fundamental to making the data itself interoperable. If the clinical trial data are structured according to international standards, data are much easier to analyse, and efforts needed for data cleaning, pre-processing and for secondary use are reduced. A common language and a common set of expectations facilitates interoperability between systems and/or devices. Objective: The main objectives of this study were to identify commonalities and differences among the clinical trial metadata, protocols, and the data collection systems/items in the VACCELERATE project. Methods: To examine the degree of interoperability achieved in the project and to suggest methodological improvements. Interoperable points were identified based on the main core outcome areas (immunogenicity, safety, efficacy/clinical/physiological) that were focused on the development of the master protocol template and were manually compared for the (i) summary, objectives, and end points in the protocols of the three VACCELERATE clinical trials (EU-COVAT-1_AGED, EU-COVAT-2_BOOSTAVAC, EU-COVPT-1_CoVacc) and the master template protocols, (ii) Metadata of all three clinical trials and (iii) through a questionnaire survey evaluations of the differences in data management system and structures that allowed data exchange in the VACCELERATE network. Results: The non-commonality identified within the protocols and metadata were due to the differences in the populations, the variations in design of the protocols and the vaccination pattern. The detailed released metadata for all three vaccine trials was clearly designed using the internal standards, terminology, and general approach of CDASH (Clinical Data Acquisition Standards Harmonisation) (Data standards for data collection, e.g., on eCRFs). VACCELERATE received a very substantial boost simply from the selection of as the single data management provider (Clinical Trials Centre Cologne (CTCC), with system database development coordinated by the same individual and without the need for coordination among different trial units which lead to a high degree of uniformity into the system automatically. Harmonised transfer of data to all sites with tried and trusted methods allowed a quick exchange and relatively secure method of transfer. Conclusions: This study illustrated that the use of master protocols can significantly increase the trial operation efficiency and the data interoperability if similar infrastructure and data management procedures are adopted and applied for multiple trials. To improve data interoperability and facilitate interpretation and analysis, shared data should be structured, described, formatted, and stored employing widely recognised data and metadata standards.

  • Background: The emergence of artificial intelligence (AI) social chatbots represents a significant development in the intersection of technology and mental health, offering potential benefits through natural and emotional communication. Social chatbots, unlike their task-oriented counterparts, focus on building relationships and providing social support, which can positively impact mental health outcomes such as loneliness and social anxiety. However, the specific effects and mechanisms through which these chatbots influence mental health remain underexplored. Objective: This study aims to explore the psychiatric potential of AI social chatbots, particularly focusing on their impact on loneliness and social anxiety among individuals in their twenties. The study seeks to: (i) assess the impact of engaging with an AI social chatbot in South Korea, "Luda Lee," on these mental health outcomes over a four-week period; and (ii) analyze user experiences to identify perceived strengths and weaknesses, as well as the applicability of social chatbots in therapeutic contexts. Methods: A single-group pre-post study was conducted with 176 students in their 20s who interacted with the chatbot over a four-week period, with mental health outcomes assessed at three intervals. The measures included loneliness, social anxiety, and mood-related symptoms such as depression. Quantitative measures were analyzed using analysis of variance and regression to identify the factors affecting change. Thematic analysis was used to analyze user experiences and assess the perceived benefits and challenges of chatbots. Results: Significant reductions in loneliness and social anxiety were observed, with loneliness showing improvement in the second week (P = .017) and social anxiety in the fourth week (P = .011). Self-disclosure and perceived usefulness were associated with a greater reduction in loneliness and social anxiety. Qualitative analysis revealed that users appreciated the chatbot's ability to provide empathy and social support, with many considering it a reliable conversation partner. However, some users noted issues such as inconsistent responses and excessive enthusiasm, which sometimes broke the immersion and affected their overall experience. Conclusions: Our observations suggest that social chatbots may have the potential to mitigate feelings of loneliness and social anxiety, indicating their possible utility as complementary resources in mental health interventions. Insights garnered from user experiences and chatbot functionalities highlight the potential benefits of empathy, constant accessibility, and thoughtful conversation structuring in supporting therapeutic objectives. Clinical Trial: Clinical Research Information Service (CRIS) KCT0009288; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=26360&search_page=L

  • Background: Digital phenotyping (DP), the process of using data from digital devices, like smartphones and wearable technology to understand and monitor people's behaviour, health, and daily activities, has shown significant promise in mental health care within high-income countries (HICs). However, its application in lower and middle-income countries (LMICs) is limited, particularly among impoverished populations such as slum residents. Objective: This study investigates the awareness, knowledge, acceptance, and implementation of DP, including willingness to share data, and concerns regarding privacy and data security, among residents of Dhaka's Korail slum, one of Bangladesh's largest and most densely populated informal settlements. Methods: We conducted eight focus group discussions (FGDs) with 38 participants with individuals diagnosed with serious mental disorders (SMDs) and their caregivers. The FGDs also included a section explaining what DP is. Results: There was a general lack of awareness about DP among the participants. Most had no prior knowledge of DP, but after receiving an explanation, they acknowledged its potential applications and benefits. Participants recognized the utility of DP for health monitoring, particularly in managing mental health conditions. They expressed their interest in sharing data, if the content of their activities was not accessed. Despite these perceived benefits, significant concerns about privacy and data security emerged. Participants expressed fears about the potential misuse of their personal information, with some feeling resigned to the idea of already being constantly monitored. This highlights a critical barrier to the adoption of DP tools: the need for robust data protection measures and transparent communication to build trust among users. Participants stressed the need for DP to reflect local customs and practices. Conclusions: To implement DP effectively in LMICs, educational initiatives are necessary to raise awareness and understanding of the technology. Additionally, robust data protection measures must be in place, and clear communication about these measures can help alleviate fears and build trust. DP tools should be adapted to fit the cultural context of the target population, possibly involving modifications to the types of data collected or the way data is interpreted. In conclusion, while DP holds potential to improve mental health care in underserved communities, addressing barriers related to awareness, privacy, culture and usability is crucial. Focusing on educational initiatives, robust data protection, cultural adaptation, user-friendly design, and community engagement, DP can become a valuable tool in bridging the mental health care gap in LMICs.

  • Current Technological Advances in Dysphagia Screening: A Systematic Scoping Review

    Date Submitted: Aug 19, 2024
    Open Peer Review Period: Aug 19, 2024 - Oct 14, 2024

    Background: Dysphagia affects more than half of older adults with dementia and is associated with a 10-fold increase in mortality. The development of accessible, objective, and reliable screening tools is crucial for early detection and management. Objective: This systematic scoping review aims to examine the current state-of-the-art in artificial intelligence (AI) and sensor-based technologies for dysphagia screening. Methods: We conducted a systematic literature search across CINAHL, Embase, PubMed, and Web of Science, performed by two independent researchers. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) framework. The methodological quality of the eligible articles was assessed using the QUADAS-2 tool, to which we added a “model” domain for a more comprehensive evaluation. Data extracted from the included studies were synthesized narratively. Results: The review included 24 studies involving 2,979 participants. Most studies focused on per-individual classification rather than per-swallow event classification. Acoustic (n = 13) and vibratory signals (n = 9) are the primary modality sources utilized in dysphagia screening. Additional sources included nasal airflow, electromyography (EMG), strain and motion analysis, and optical methods. Notably, six studies employed multimodal approaches, while the remaining 18 studies focused on a single modality. Machine learning models, particularly support vector machines (n = 13), were frequently utilized, while deep learning approaches have been gaining traction. Performance metrics varied widely across studies, with some reporting high accuracy and AUC values, despite small testing samples. Multimodal systems appeared to perform better than unimodal systems. The methodological quality assessment revealed a high risk of bias in many studies, particularly in patient sampling, blinding procedures, and model development. The lack of external validation and domain adaptation testing raises concerns about the transferability and real-world applicability of these AI-based systems. Conclusions: This review provides an overview of technological advancements in the use of artificial intelligence and sensors for dysphagia screening. These promising developments in screening tools pave the way for continuous long-term tele-swallowing assessments.

  • The Current Status of Large Language Models in Summarizing Radiology Report Impressions: Evaluation Study

    Date Submitted: Aug 19, 2024
    Open Peer Review Period: Aug 19, 2024 - Oct 14, 2024

    Background: Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various natural language processing tasks, particularly in text generation. However, their effectiveness in summarizing radiology report impressions remains uncertain. Objective: This study aims to evaluate the capability of eight LLMs, i.e., Tongyi Qianwen, ERNIE Bot, ChatGPT, Bard, Baichuan, ChatGLM, HuatuoGPT, and ChatGLM-Med, in summarizing radiology report impressions. Methods: We collected three types of radiology reports, i.e., CT, PET-CT, and Ultrasound reports, from Peking University Cancer Hospital and Institute. Using these reports, we created zero-shot, one-shot, and three-shot prompts with or without complete example reports as inputs to generate impressions. We employed both automatic quantitative evaluation metrics and five human evaluation metrics (completeness, correctness, conciseness, verisimilitude, and replaceability) to assess the generated impressions. Two thoracic surgeons (ZSY and LB) and one radiologist (LQ) compared the generated impressions with reference impressions, scoring them according to the five human evaluation metrics. Results: In the automatic quantitative evaluation, ERNIE Bot, Tongyi Qianwen, and ChatGPT demonstrated the best performance in generating impressions for CT, PET-CT, and Ultrasound reports, respectively. For the human semantic evaluation, ERNIE Bot outperformed the other LLMs in CT and Ultrasound impression generation, while Tongyi Qianwen excelled in PET-CT impression generation. The generated impressions were generally complete and correct but lacked conciseness and verisimilitude. Although one-shot and few-shot prompts improved conciseness and verisimilitude, clinicians noted a significant gap between the generated impressions and those written by radiologists. Conclusions: Current large language models can produce radiology impressions with high completeness and correctness but fall short in conciseness and verisimilitude, indicating they cannot yet fully replace impressions written by radiologists.

  • Background: With obesity posing an increasing public health challenge, the demand for personalized weight loss and fitness solutions has intensified. Given advancements in artificial intelligence, like GPT-4, this study evaluates its effectiveness as a virtual fitness coach for creating personalized plans. Method: We selected a 24-year-old female from Chongqing, China, providing detailed personal information. Using this, both GPT-4 and three local professional coaches formulated 16-week fitness plans. Senior health science experts evaluated these plans across four dimensions: personalization, effectiveness, comprehensiveness, and safety. Descriptive and inferential statistics were employed for analysis. Results: GPT-4 excelled in personalization (M=12.80, SD=0.84) compared to coaches (M=11.53, SD=0.46). However, coaches slightly outperformed in effectiveness (GPT-4: M=12.60; Coaches: M=12.80), safety (GPT-4: M=12.20; Coaches: M=12.33), and comprehensiveness (GPT-4: M=12.00; Coaches: M=12.13), with no significant differences (P > .05). These findings highlight comparable performance but suggest potential for GPT-4 in personalized exercise prescriptions. Conclusions: GPT-4 shows promise as a virtual fitness coach for personalized weight loss and fitness guidance. Yet, due to technological constraints, it cannot fully replace human coaches. Future research should explore enhancing AI models' applicability in sports and their collaboration with coaches for optimal personalized fitness solutions.

  • Background: The integration of connected medical devices (cMDs) in healthcare brings benefits but also introduces new, often challenging-to-assess risks related to cybersecurity, which have the potential to harm patients. Current regulations in the EU and US mandate the consideration of these risks in the benefit-risk analysis (BRA) required for medical device (MD) approval. This important step in the approval process weighs up all defined benefits of a device with its anticipated risks to ensure that the product provides a positive argument for use. However, there is limited guidance on how cybersecurity risks should be systematically evaluated and incorporated into the BRA. Objective: This scoping review aims to identify current legal frameworks, guidelines, and standards in the US and EU on how cybersecurity-related risks should be considered in the BRA of medical devices. Based on those documents, this review provides recommendations for manufacturers and regulators. Methods: This scoping review follows the PRISMA-ScR framework. A systematic literature search of three databases was conducted on July 3rd, 2024: FDA guidance, International Medical Device Regulators Forum (IMDRF), and Medical Device Coordination Group (MDCG). Search terms included ‘cybersecurity,’ ‘security,’ benefit/risk,’ ‘benefit-risk,’ and ‘risk-benefit.’ Additional references were identified via citation search and expert interviews. Inclusion criteria were met if a document was a guideline or standard in force that provided guidance on BRA or cybersecurity-related risks of medical devices. Documents were excluded when they did not describe MDs, when they were limited to a subclass of devices, when they were about in vitro diagnostic medical devices or investigational devices, and when the content of the source was insufficient to undertake a scientific analysis. Data was extracted and analysed using MAXQDA 2022, and findings were narratively summarised and visualised in figures and tables. Results: The search identified 41 documents, with 21 meeting the inclusion criteria. These documents included two regulations, four standards, five technical reports, and ten guidance documents. The review revealed that while cybersecurity risks are acknowledged, detailed methods for their integration into the BRA are lacking. Some standards and guidelines provide superficial examples of security BRAs, but a comprehensive and standardised approach remains absent. Conclusions: This review highlights a significant gap between the recognition of cybersecurity risks in cMDs and the guidance on their incorporation into the BRA. Standardised frameworks are needed to provide clear methods for evaluating cybersecurity-related risks and their impact on the safety and security of MDs. The recommendations proposed in this review aim to bridge this gap and support the development of more robust BRA practices that enhance patient safety and device effectiveness.

  • Background: Social media platforms offer valuable insights into the patient’s experience, revealing organic conversations that reflect their immediate concerns and needs. Through active listening to lived experiences, we can identify unmet needs and discover real-world challenges patients and caregivers face. Objective: This study aimed to develop a reusable framework to collect and analyze evolving social media data, capturing insights into the experiences of individuals with MDS and higher-risk myelodysplastic syndromes (HR-MDS) and their caregivers. The findings can inform the development of appropriate patient support interventions. Methods: We conducted an extensive Google search to identify social posts of interest using validated URLs and keywords on English-language websites relevant to MDS. The search covered the period from 1/1/2008 to 12/31/2022. We utilized scraping algorithms to collect, clean, and standardize pertinent information. To classify the perspective of each experience as either that of a patient or caregiver, we employed classification algorithms. This involved contextualizing and summarizing all user posts, followed by decision tree tagging to assign them to the patient or caregiver category. Advanced algorithms were employed to analyze the semantic and temporal structure of the data. Patients or caregivers were categorized as HR-MDS based on contextual mentions of high-risk in their posts or specific factors aligned with NCCN guidelines (e.g., blast percentage, transplantation, use of high-intensity chemotherapy or hypomethylating agents, or disease progression). Each post was assigned major themes and sentiments using a supervised classification machine learning model. Additionally, we employed a semi-supervised machine learning approach for the identification of latent themes in the data corpus. Results: The data collected comprised approximately 5.5 million words from 42,000 posts across 5,500 threads, involving about 4,000 users predominantly from the US, UK, and Canada. Out of the 1,249 users classified as HR-MDS, 588 (47%) were patients and 661 (53%) were caregivers. Among the HR-MDS users, the predominant sentiments included concern (78%), anxiety (60%), frustration (58%), fear (58%), and confusion (49%). Concern was the predominant sentiment expressed by caregivers (n=971, 59%), and anxiety by patients (n=752, 55%). Common concerns were specifically related to blood counts (n=677, 54%), burden of the disease (43%), QoL (36%), available treatment options and effectiveness (31%), and disease progression and prognosis (31%). Anxiety related to health and disease (48%), treatment (26%), and the diagnostic process (20%) were also common. The most common sentiments related to fear were the potential development of health complications and the manifestation of symptoms (19%) and the progression and exacerbation of MDS (19%). Additionally, confusion was pervasive among participants, with 295 (24%) individuals finding it challenging to comprehend the nuances of MDS and its diagnosis. A systematic analysis of the principal domains for which information is being sought about HR-MDS revealed frequent mention amongst users of acquiring information on therapeutic intervention (19%), and an interest in ongoing research associated with the disease (17%) Conclusions: The application of sophisticated NLP techniques demonstrates promise in effectively identifying the emerging complex themes and sentiments experienced by HR-MDS users, thereby highlighting the unmet needs, barriers, and facilitators associated with the disease. Clinical Trial: NA

  • Background: Large Artificial Intelligence (AI) language models have been increasingly applied in the medical field for disease prediction, diagnosis, and evaluation. However, research on AI-assisted early sepsis identification and screening remains scarce. Here, we conduct a retrospective study to evaluate the diagnostic efficacy of a novel medical large language model-MedGo developed by our collaborating team and us in early sepsis in emergency department (ED). Objective: This study aims to evaluate the performance of a novel medical artificial intelligence large language model, MedGo, in supporting clinical decision-making for emergency department patients with suspected sepsis, specifically focusing on its diagnostic accuracy, comprehensiveness, readability, and analytical capabilities compared to physicians with varying levels of experience. Methods: We retrospectively collected medical history data from 203 eligible patients treated at a tertiary teaching hospital between January 1, 2023 and January 1, 2024. MedGo’s performance was compared to that of junior and senior ED physicians across nine assessment tasks related to the diagnosis and management of sepsis . A five-point Likert scale was used to assess the four dimensions of accuracy, comprehensiveness, readability and case analysis skills. Results: MedGo exhibited diagnostic performance comparable to senior doctors, scoring 4 on the Likert Scale for accuracy, comprehensiveness, readability, and analytical capability, significantly surpassing junior doctors. Furthermore, MedGo's decision support enhanced both junior and senior doctors' diagnostic abilities, with junior doctors' performance equal that of seniors. Notably, MedGo consistently delivered exceptional results in diagnosing early sepsis cases of varying severity. Conclusions: MedGo demonstrates remarkable diagnostic efficacy in early sepsis, effectively supporting clinicians of diverse experience levels in making informed decisions in the time-urgent ED. Although we acknowledge its limitations and emphasize the importance of comprehensive, standardized, systematic, and visualized medical history data in future research endeavors, the results underscore the potential of MedGo as a supportive tool in ED settings, thereby laying the groundwork for future developing specialized sepsis models.

  • In the Face of Confounders: Atrial Fibrillation Detection – Practitioners vs. ChatGPT

    Date Submitted: Aug 14, 2024
    Open Peer Review Period: Aug 16, 2024 - Oct 11, 2024

    Background: Atrial fibrillation (AF) is the most common arrhythmia in clinical practice, yet interpretation concerns among healthcare providers persist. Confounding factors contribute to false-positive and false-negative AF diagnoses, leading to potential omissions. Artificial intelligence advancements show promise in electrocardiogram (ECG) interpretation. Objective: We sought to examine the diagnostic accuracy of ChatGPT-4omni (GPT-4o), equipped with image evaluation capabilities, in interpreting ECGs with confounding factors and compare its performance to that of physicians. Methods: Twenty ECG cases, divided into Group A (10 cases of AF or atrial flutter) and Group B (10 cases of sinus or another atrial rhythm), were crafted into multiple-choice questions. Total of 100 practitioners (25 from each: emergency medicine, internal medicine, primary care, and cardiology) were tasked to identify the underlying rhythm. Next, GPT-4o was prompted in five separate sessions. Results: GPT-4o performed inadequately, averaging 3 (±2) in Group A questions and 5.40 (±1.34) in Group B questions. Upon examining the accuracy of the total ECG questions, no significant difference was found between GPT-4o, internists, and primary care physicians (p = 0.952 and = 0.852, respectively). Cardiologists outperformed other medical disciplines and GPT-4o (p < 0.001), while emergency physicians followed in accuracy, though comparison to GPT-4o only indicated a trend (p = 0.068). Conclusions: GPT-4o demonstrated suboptimal accuracy with significant under- and over-recognition of AF in ECGs with confounding factors. Despite its potential as a supportive tool for ECG interpretation, its performance did not surpass that of medical practitioners, underscoring the continued importance of human expertise in complex diagnostics.

  • Background: To understand the acceptance of healthcare technology for older adults, the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) is commonly used. However, the divergence in the current literature makes it difficult to predict acceptance and understand how various factors affect older adults’ behavior. Objective: This study aims to 1) determine the influence of perceived usefulness (PU), perceived ease of use (PEOU), and social influence (SI) on the behavioral intention (BI) to use healthcare technology among older adults and 2) and assess the moderating effects of age, gender, geographic region, type of healthcare technology, and the presence of visual demonstrations on these three pairwise relationships. Methods: Google Scholar, Web of Science, Scopus, IEEE Xplore, and ProQuest electronic databases were searched from inception to February 2024. Two independent reviewers screened the titles, abstracts, full texts, and performed data extraction and risk of bias assessments with the Newcastle-Ottawa Quality Assessment Scale. The "meta" package in R was used for data synthesis, conducting random-effects meta-analyses, meta-regression and subgroup analysis. Results: 41 studies with a total of 11,574 participants were included. Random-effects meta-analyses showed significant positive correlations for PU-BI (r = 0.607, 95% CI 0.543 - 0.665, P < .001), PEOU-BI (r = 0.525, 95% CI 0.462 - 0.583, P < .001), and SI-BI (r = 0.551, 95% CI 0.468 - 0.624, P < .001). Moderator analyses indicated significant differences in effect sizes based on geographic region for PEOU-BI (Q-test, P = .04), type of technology for PU-BI (Q-test, P = .04) and SI-BI (Q-test, P = .002), and presence of visual demonstrations for PU-BI (Q-test, P = .03) and SI-BI (Q-test, P = .04). Conclusions: The findings indicate that PU, PEOU, SI significantly impact the acceptance of healthcare technology among older adults, with heterogeneity influenced by geographic region, type of technology, and presence of visual demonstrations. Researchers should account for these variables when interpreting previous research and embarking on new studies with the TAM or UTAUT model for older adults. Clinical Trial: Current paper is not RCT

  • The Impact of Digital Isolation on Dementia Risk Among Older Adults: Findings from a Longitudinal Cohort Study

    Date Submitted: Aug 14, 2024
    Open Peer Review Period: Aug 16, 2024 - Oct 11, 2024

    Background: Dementia poses a significant global health challenge, characterized by progressive cognitive decline and functional impairment. With the aging global population, dementia prevalence is projected to surge, reaching an estimated 153 million cases by 2050. While the impact of traditional social isolation on dementia risk has been extensively studied, the influence of digital isolation—a phenomenon unique to the digital age—remains underexplored. Objective: This study investigates the association between digital isolation and dementia risk among older adults, hypothesizing that higher levels of digital isolation significantly increase the risk of developing dementia. Methods: We conducted a longitudinal cohort study using data from the National Health and Aging Trends Study (NHATS), analyzing 8,189 participants aged 65 and older from the third (2013) to the twelfth wave (2022). Digital isolation was quantified using a composite Digital Isolation Index, derived from participants' usage of digital devices, electronic communication, internet access, and engagement in online activities. Participants were stratified into low isolation and moderate to high isolation groups. Dementia incidence was assessed using cognitive tests and proxy reports. Cox proportional hazards models were employed to estimate the association between digital isolation and dementia risk, adjusting for potential confounders including sociodemographic factors, baseline health conditions, and lifestyle variables. Results: The moderate to high isolation group demonstrated a significantly elevated risk of dementia compared to the low isolation group. In the discovery cohort, the adjusted hazard ratio (HR) was 1.25 (95% CI: 1.03-1.52, P=0.023), while the validation cohort showed an HR of 1.68 (95% CI: 1.30-2.16, P<0.001). The pooled analysis across both cohorts revealed an adjusted HR of 1.40 (95% CI: 1.20-1.64, P<0.001). Kaplan-Meier curves corroborated a higher incidence of dementia in the moderate to high isolation group. Conclusions: Our findings indicate that digital isolation is a significant risk factor for dementia among older adults. This study underscores the importance of digital engagement in mitigating dementia risk and suggests that promoting digital literacy and access to digital resources should be integral components of public health strategies aimed at dementia prevention.

  • Background: Social media is currently serving as a tool for increased digital interconnectedness and has resulted in playing an instrumental role in sharing health-related information with a wide audience. In conjunction with the vast availability of information, there has been a rapid spread of misinformation, leading to public mistrust, safety concerns, and discrimination. The COVID-19 pandemic has amplified the threat of misinformation resulting in detrimental health outcomes due to individuals becoming fatigued with COVID-19 health guidance. Although vaccinations are the key to combating COVID-19, the overwhelming amount of misinformation has resulted in diminished vaccine acceptance. Objective: (1) Train and deploy a group of healthcare workers and student volunteers to address anti-vaccine sentiment on Facebook; and, (2) Evaluate the intervention through semi-structured interviews to determine lessons learned and suggestions for future initiatives to address misinformation online Methods: The project utilized volunteers to address vaccine-hesitant comments on Facebook (Met Platforms Inc., Menlo Park, California), with the overall goal of empowering healthcare professionals to engage with vaccine-hesitant individuals online to counteract the spread of vaccine misinformation. Eligible participants included healthcare workers and students in healthcare-related disciplines were recruited through social media and email advertising campaigns by the University of Calgary, School of Nursing contact list. Informational training sessions on Zoom with a duration of 30-minutes followed, to better equip volunteers with the ability to utilize their working knowledge of health communication and behaviour change to correct online misinformation. During the deployment of volunteers, they were provided a file containing Facebook posts that discussed COVID-19 vaccines to act as a starting point for leaving or responding to comments that spread vaccine misinformation. Participants in the project were provided with working knowledge of health communication, behaviour change, and correct misinformation through the informational training sessions. Qualitative evaluation in the form of interviews were used to examine participant experiences, where it was found that volunteers felt that they were adequately equipped to engage in vaccine conversations both online and in healthcare settings. Overall, the project has addressed vaccine hesitancy and valuable insights into the relationship between public engagement and communication in the era of digital interconnectedness. Results: Following the evaluative interview discussions, three main themes emerged regarding the project’s format and training model, the factors motivating volunteers to participate, and overall experiences tackling misinformation on an online platform. The first theme showcased that the training format was effective due to its use of interactive components and overall flexibility, resulting in it being well-received by volunteers. The second identified theme highlighted that a main driving factor for participation included a balance of professional development and societal good. Finally, the third theme revealed that the volunteers' experiences in interacting with the public revealed a rich tapestry of emotions and perspectives, where vaccine hesitancy is interconnected with emotional responses and personal beliefs. Conclusions: The Informed Choice Project provided an opportunity to increase self-efficacy and confidence for more than a dozen healthcare professionals and students while engaging in vaccine-related conversations online. Immediate challenges associated with the COVID-19 pandemic, including combating misinformation and promotion of vaccination were addressed, which has resulted in a more reliable groundwork for shaping future public health communication strategies. To enhance both participant satisfaction and compliance, future interventions should consider utilizing a self-paced format, flexible hours, and highlight the vitality of healthcare professionals as key advocates for trusted sources of information for the public.

  • Generative Artificial Intelligence in Medicine: A Mixed Methods Survey of UK General Practitioners

    Date Submitted: Aug 13, 2024
    Open Peer Review Period: Aug 15, 2024 - Oct 10, 2024

    Background: Since November 2022, with the debut of OpenAI’s ChatGPT, there has been growing interest in the use of generative artificial intelligence (AI), including in healthcare. However, there is only limited research into doctors’ adoption of these tools and their opinions about their application in clinical practice. Objective: This study aimed to explore the opinions of general practitioners (GPs) in the United Kingdom (UK) about the use of generative AI tools (ChatGPT/Bard/Bing AI) in primary care. Methods: Between February 2nd-24th 2024, using a convenience sample, we administered a web-based mixed methods survey of 1000 GPs in the UK to explore their experiences and opinions about the impact of generative AI on clinical practice. Participants were recruited from registered GPs currently working in the UK using the clinician marketing service Doctors.net.uk. Quantitative data were analyzed using descriptive statistics and nonparametric tests. We used thematic content analysis to investigate free-text responses and conducted a qualitative descriptive analysis of written responses (“comments”) to 2 open-ended questions embedded in the web-based questionnaire. Results: A total of 1006 GPs responded, with 53% being male and 54% aged 46 years and older. Most GPs (80%) expressed a need for more support and training in understanding these tools. GPs at least somewhat agreed AI would improve documentation (59%), patient information gathering (56%), treatment plans (41%), diagnostic accuracy (40%), and prognostic accuracy (38%). Additionally, 62% believed patients might rely more on AI, 55% felt it could increase inequities, and 54% saw potential for patient harm, but 47% believed it would enhance healthcare efficiency. GPs who used these tools were significantly more optimistic about the scope for generative AI in improving clinical tasks compared with those who did not report using them. Elaborating on the quantitative component of the survey, 31% (307/1006) left comments that were classified into 4 major themes in relation to generative AI in medicine: (1) lack of familiarity and understanding, (2) a role in clinical practice, (3) concerns, and (4) thoughts on future of healthcare. Conclusions: This study highlights UK GPs' perspectives on generative AI in clinical practice, emphasizing the need for more training. Many GPs reported a lack of knowledge and experience with this technology and a significant proportion used non-medical grade technology for clinical tasks, with the risks that this entails. Medical organizations must urgently invest in educating and guiding physicians on AI use and limitations.

  • Background: Digital health interventions, such as electronic immunization registries (eIR) and electronic Logistic Management Information Systems (eLMIS), have the potential to significantly improve immunization data management and vaccine logistics in low- and middle-income countries (LMICs). Despite their growing adoption, there is limited evidence on the financial and economic costs associated with their implementation compared to traditional paper-based systems. Objective: We aimed to measure the costs of implementing and maintaining eIR and eLMIS systems in LMICs, and to estimate the affordability of their implementation as compared to the previous paper-based registries. Methods: The study was conducted across four countries: Guinea, Honduras, Rwanda, and Tanzania. A combination of primary and secondary data sources was used for the analysis. Expenditure information regarding the design, development and implementation of the tools was directly obtained from implementers and National Immunization Program offices in all countries. Primary survey data was collected to gauge the operational expenses of immunization information systems, both with and without electronic tools using an Activity Based Costing approach. The cost of immunization information system to the national level was then extrapolated and compared to national spending on immunization as a measure for affordability. Results: The total costs of designing, developing and deploying eIR and/or eLMIS were I$ 1.7, 5.4, 4.7 and 33 million in Guinea, Honduras, Rwanda and Tanzania respectively. Design costs were greatly affected by the degree of customization of the tool, whereas roll out costs were mostly driven by the costs of purchasing hardware and training of health workers. Overall, the implementation of the electronic systems was associated with higher costs in Honduras (I$ 535 per facility, 95% CI 441; 702) and Rwanda (I$ 278, 95%CI 75; 482), a cost reduction in Tanzania (I$ -1,770, 95%CI -2,990; -550) and no significant cost difference in Guinea. The percentage weight of the cost of managing data with the electronic systems over the total national immunization budgets was estimated at 8.6%, 1.1%, 3.7% and 1.8% for Honduras, Rwanda, Tanzania and Guinea, respectively Conclusions: Digital health interventions such as eIR and eLMIS can potentially reduce costs and improve the efficiency of immunization data management and vaccine logistics in LMICs. However, the extent of cost savings is contingent upon the degree to which these digital systems replace traditional paper-based methods. Our study suggests that the economic impact of digital health solutions greatly depends on factors such as infrastructure, implementation, and the extent to which these technologies are integrated into existing healthcare systems. Careful planning and investment are essential to realizing the full economic potential of digital health in LMICs.

  • Background: Obesity is a chronic complex disease associated with increased risks of developing several serious and potentially life-threatening conditions. It is a growing global health issue. Pharmacological treatment is an option for patients living with overweight or obesity. Digital technology may be leveraged to support patients with weight loss in the community, but it is unclear which of the multiple digital options are important for success. Objective: This systematic review and component network meta-analysis aimed to identify components of digital support for weight loss interventions that are most likely to be effective in supporting patients to achieve weight loss goals. Methods: We searched MEDLINE, Embase, APA PsycInfo and Cochrane Central Register of Controlled Trials, inception to November 2023, for randomised controlled trials in adults with BMI ≥25 (≥23 for Asian populations) using any weight loss intervention with digital components, with weight loss outcomes. Eligible trials were prioritised for synthesis based on intervention relevance and duration, and target population. Trial arms with substantial face to face elements were de-prioritised. Prioritised trials were assessed for quality using the Cochrane Risk of Bias tool v1. We conducted an Intervention Component Analysis to identify key digital intervention features and coding framework. All prioritised trials arms were coded using this framework and into a component network-meta-analysis. Results: Searches identified 6528 reports of which 119 were included. After prioritisation, 151 arms from 68 trials were included in the synthesis. Nine common digital components were identified from 151 trial arms: provision of information and education, goal setting, provision of feedback, peer support, reminders, challenge, and competition, contact with a specialist, self-monitoring, incentives and rewards. Of these, three were identified as ‘best bets’ because they were consistently and numerically, but not usually statistically significantly most likely to be associated with weight loss at 6 and 12 months. These were patient information, contact with a specialist and incentives/rewards. An exploratory model combining these three components was statistically significantly associated with successful weight loss at 6 (-2.52 kg, 95% CI [-4.15, -0.88]) and 12 (-2.11 kg, [-4.25, 0.01]) months. No trial arms used this specific combination of components. Conclusions: Our findings indicate that the design of digital interventions to support weight loss should be carefully crafted around core components. On their own, no one digital component could be considered essential for success, but a combination of information, specialist contact and incentives warrants further examination. Clinical Trial: PROSPERO:IDCRD42023493254 https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023493254

  • Mapping Research on Smart Hospitals: A Data-Driven Exploration Through Bibliometrics and Text Mining

    Date Submitted: Aug 15, 2024
    Open Peer Review Period: Aug 15, 2024 - Oct 10, 2024

    Objective: This study conducts a comprehensive bibliometric analysis to explore the landscape of research on smart hospitals, highlighting significant research trends, challenges, and future directions. Methods: We analyzed 5,190 publications from the Web of Science and PubMed databases spanning January 2014 to October 2023, employing text mining and Latent Dirichlet Allocation (LDA) for data categorization into four domains: technological application in healthcare ("Function"), implementation challenges ("Barrier"), operational impacts ("Supply"), and patient outcomes ("Demand"). Results: Our findings show a surge in smart hospital publications post-2016 and following the onset of the COVID-19 pandemic. Sixty topics were defined by LDA analysis, and the "Function" domain was the most researched, indicating strong interest in leveraging technology for improved healthcare delivery. However, there was a significant gap in the "Demand" domain, suggesting a need for more patient-centric research. Thematic mapping and co-occurrence analysis highlighted key technological progress and important challenges, including the need for better interoperability and stronger data security. Additionally, a thorough review of key articles emphasized the practical use of these technologies, underlining the importance of increasing patient engagement and tailoring healthcare services to individual needs. Conclusion: This study reveals a focus on digital technology applications in hospitals but highlights a critical gap in evaluating their effectiveness and developing management-oriented frameworks for smart hospital analysis. It underscores the need for further exploration of the effect of advanced digital technologies and increased managerial research to enhance hospital services and efficiency.

  • Factors influencing the use of online symptom checkers in the United Kingdom: A cross-sectional study

    Date Submitted: Aug 12, 2024
    Open Peer Review Period: Aug 14, 2024 - Oct 9, 2024

    Background: Background The global shortage of healthcare workers has exacerbated the challenges faced by health systems worldwide. The World Health Organization (WHO) projects a deficit of 10 million healthcare workers by 2030. While low- and lower-middle income countries will be the most affected, all countries will face significant challenges [1]. In the UK, the National Health Service (NHS) is already under considerable strain [2], with significant effects on the quality of care and health outcomes of patients [3]. At the same time, there is a growing demand for health information and increasing consumer empowerment [4]. In this context, the reliance on digital health tools has surged, particularly decision support tools including online symptom checkers (OSCs) [5, 6]. The ubiquitous access to the internet supports this trend, with 96% of households in the UK having internet access in 2020 [7], which accelerated following the advent of the COVID-19 pandemic given the need of health services to avoid face-to-face contact and preserve urgent care capacity [8]. Symptom checkers are available as websites or applications and can generate a prioritised list of potential diagnoses based on the entered symptoms and suggest suitable actions, such as self-care, consulting a general practitioner (GP), or seeking urgent medical care [9]. By providing preliminary diagnostic guidance and triage recommendations, these tools could potentially alleviate some of the burdens on healthcare systems with the potential to reduce unnecessary healthcare visits by providing timely medical advice and empowering individuals to make informed decisions about their health [10, 11]. The diagnostic and triage accuracy have been found to vary greatly among OSCs, thus raising concerns and calling for caution [12, 13]. The use of OSCs globally varies as: users tend to be young [12, 13], women [6, 14, 15] and more highly educated [6, 14-16]. Having a chronic health condition has also been associated with greater use in one study [16]. A 2022 systematic review on user experience of symptom checkers identified eight relevant aspects of user experience that have been explored in the literature, including motivation, trust, acceptability, satisfaction, accuracy, usability, safety/security and functionality [17]. Of the 31 included studies only 3 were carried among the UK population [18-20] and focused either on cancer symptom checking [19], users with inflammatory arthritis [20] or the use of one specific OSC [18]. Data on the experience and perspectives of potential users living in the UK on the use of OSCs remains scarce and no study has yet quantified or ranked the various factors associated with the use of OSCs. Objective: The aim of this study was to identify, characterise and quantify the factors associated with the use of OSCs among community-dwelling adults in the UK. Specifically, we sought to (i) assess the demographic characteristics of OSC users, (ii) evaluate user perceptions of the usability and effectiveness of OSCs, (iii) identify concerns related to the privacy, security and accuracy of OSCs, and (iv) quantify the weight of these various factors on the adoption and utilisation of OSCs. Methods: Study design This cross-sectional study aimed to explore factors influencing the use of OSCs among community-dwelling adults in the UK. The study employed a quantitative methodology using an electronic survey tool (eSurvey). Data collection This was an open eSurvey, accessible to anyone with the survey link, and required less than 10 minutes to complete. The eSurvey was developed and tested to ensure clarity, usability and technical functionality before fielding. The link to the eSurvey was active on the Imperial College Qualtrics platform between 23 February and 25 March 2024. Study information was disseminated including the Participant Information Sheet (PIS) and link to the survey. Participants were recruited through convenience sampling. The researcher’s personal and professional networks were mobilised to respond and further disseminate the eSurvey among potentially eligible participants. Most participants were recruited via Prolific Academic’s panel [21]. The PIS included information regarding the study’s aims, the protection of participants’ personal data, their right to withdraw from the study at any time, which data were stored, where and for how long, who the investigator was, the purpose of the study and survey length. Participants were informed that this was a voluntary survey. Informed consent was obtained from all participants. Data collected were stored on a secure database at Imperial College London and only accessible to the researcher team. All responses were pseudo-anonymised to ensure confidentiality. The selection of factors which may affect the use of OSCs to be included in the survey was guided by a review of existing literature of the topic. Demographic factors including age and gender, as well as perceptions such as perceived usability, effectiveness, reliability, accuracy, safety and privacy have been identified as influential factors from prior studies [6, 16-18, 20, 22]. Before publication, the survey was tested, piloted and revised internally by the study team. In its final version, the survey comprised a total of 25 questions displayed across four screens and gathered data regarding respondents’ awareness, use and perspectives regarding OSCs, as well as basic demographic information (Table 1). Data analysis Only questionnaires fully completed were included in the analysis. Duplicate entries from the same IP address within a 24-hour period were also eliminated before analysis. Participant characteristics and responses were summarised using total (n) and relative (%) frequencies. For inferential analyses, 'strongly agree' and 'somewhat agree' were categorised into 'agree,' and 'strongly disagree' and 'somewhat disagree' were categorised into 'disagree, to create binary variables for logistic regression analysis. Relationships between (i) demographic factors, (ii) usability and effectiveness, (iii) reliability and accuracy, (iv) risks and concerns and the use of OSC were assessed using univariable and multivariable logistic regression models, adjusting for age, gender, ethnicity, education level, parenting status, disability and long-term health conditions. Results were deemed statistically significant at a p-value <0.05. The odds ratios for these relationships were quantified to understand the influence of each factor on the use of OSCs and compare them. All analyses were performed using STATA, version 17 (StataCorp LP, College Station, TX, USA). The Checklist for Reporting Results of Internet E Surveys (CHERRIES) was used to guide reporting [23] (Supplementary file 1). Results: Participants characteristics A total of 641 individuals took part in the survey with complete responses obtained from 634 respondents. A full description of participants according to age, gender, ethnicity and educational background is provided in Table 2. The largest proportion were between 26 and 35 years old (32.7%), followed by participants aged 36-45 (24.6%). Gender distribution showed a slight predominance of females (46.5%) over males (41.8%), with a smaller percentage identifying as "other" (10.3%). Ethnicity predominantly comprised individuals identifying as White (84.2%), followed by Asian/Asian British (7.1%) and Black/African, Caribbean/Black, British (3.9%). The majority held a college or university degree (71.5%), nearly a third (31.5%) had children under 16 years, 15.1% of respondents reported having disabilities, and 22.9% had one or more long-term health conditions. Main survey findings Use of OSC The majority (85.7%) of participants had used an OSC, while 14.4% reported never having used one. The reasons for non-use of OSC reported by the largest proportion (45.1%) was preference of consulting a healthcare professional directly rather than using OSC, followed by having never heard of them (39.6%); 24.2% of respondents did not trust them. However, two-thirds of the non-users (64.8%) expressed a likelihood of using a symptom checker in the future (Supplementary Table 1). The most utilised OSC was NHS 111, with 78.6% of respondents indicating that they had used this tool, followed by Healthline (22%). Participants predominantly used symptom checkers before seeking medical advice (94.5%), primarily to better understand symptoms (79.0%) and to determine the need for medical care (77.4%). Most respondents (77.9%) indicated that symptom checkers offered recommendations for action or triage. Among those who received such recommendations, a significant proportion (79.7%) were directed to seek a consultation with a healthcare professional. Regarding adherence, most participants (59.8%) reported following the recommendations most of the time, 27.7% stated they always adhered to the suggestions provided by the symptom checker, while a smaller proportion (12.3%) admitted to rarely doing so. Association between demographics and use of OSC An increase in age was associated with a decrease in OSC use. Specifically, individuals aged 46-55, 56-65 and >65 showed significantly decreased odds of using OSC (adjusted odds ratio (aOR)=0.29, 95%CI 0.11 - 0.72), (aOR=0.27, 95%CI 0.10 - 0.71) and (aOR=0.22, 95%CI 0.06 - 0.78) respectively, compared to the younger (18-25) age group. Similarly, females exhibited higher odds of using OSC compared to males (aOR=1.79, 95%CI 1.05 – 3.06). Having children under 16 years of age also showed significantly higher odds of using OSC (aOR= 3.19, 95%CI 1.56 - 6.51) compared to those who do not have children under 16 years of age. In contrast, neither ethnicity nor educational background exhibited any statistically significant association with the use of OSC. Similarly, disability and long-term health conditions did not contribute to the outcome of using OSC; Table 3. Associations between OSC use & users’ perceptions of OSCs The main survey findings are shown in Supplementary Table 1. The segment below highlights key associations between perceived usability and effectiveness, reliability and accuracy, and risks and concerns with using OSCs Perceived usability and usefulness and use of OSCs Most of the participants found OSCs easy to use (89.3%), believed they could help with medical decisions (86.0%) and to support their health literacy and self-care (84.7%). A great proportion also (94.6%) agreed that OSCs are useful tools in scenarios with limited access to healthcare professionals, such as rural settings or out-of-hours situations. Participants who found the symptom checkers easy to use were more than eight times more likely to utilise them compared to those who did not (aOR=8.17, 95% CI 4.25-15.71). Similarly, individuals who found these tools helpful in making better medical care choices were almost three times more likely to use them than those who did not (aOR=2.96, 95% CI 1.62-5.42). Moreover, those who agreed that the symptom checkers improved health literacy and supported self-care showed a heightened likelihood of use (aOR=2.36, 95% CI 1.30-4.28). Participants who perceived the OSC as useful in scenarios with limited access to healthcare professionals were twice as likely to utilise them compared to those who disagreed (aOR=2.15, 95% CI 1.01-4.59); Table 3. Perceived reliability and accuracy and use of OSCs Just over half of the respondents expressed confidence and trust in OSCs’ information (57.3%). The perceived accuracy of OSCs’ diagnosis was slightly higher (63.0%) while 79.2% of respondents said they trusted the triage provided by the tools. 69.2% of respondents found using a symptom checker reassuring and made them feel less anxious about their health. Participants who trusted the suggested diagnosis to be accurate were over twice as likely to use the symptom checkers compared to those who disagreed (aOR=2.24, 95%CI 1.32-3.79). Similarly, individuals who trusted the triage recommendation provided by these tools showed a heightened likelihood of use (aOR=2.33, 95% CI 1.33-4.06). Participants who found using symptom checkers reassuring and anxiety-reducing were significantly more likely to utilise them (aOR=3.85, 95% CI 2.28-6.50). Additionally, the encouragement from family and friends to use symptom checkers significantly influenced their use (aOR=2.05, 95%CI 1.24-3.41). However, encouragement from GPs was not significantly associated with the use of symptom checkers (aOR=1.34, 95%CI 0.79-2.24); Table 4. Perceived risks and concerns and use of OSCs Reported concerns included safety (75.9%), privacy (41.7%) and exacerbating inequalities (41.6%). More than half of the respondents (65.9%) worried about replacing traditional consultations and more than a quarter (26.9%) would not feel confident discussing the outcomes of their symptom checker consultation with their GP. Participants who agreed that symptom checkers are not yet safe enough to rely solely on them and may put their health at risk did not show a significant association with use compared to those who disagreed (aOR=0.59, 95% CI 0.30-1.17). However, concerns regarding privacy and health information security were significantly inversely associated with use, with individuals expressing such worries being less likely to utilise symptom checkers (aOR=0.58, 95% CI 0.35-0.97). Similarly, those who believed that symptom checkers might increase inequalities between patients were less likely to use them (aOR=0.47, 95% CI 0.28-0.79). Concern about OSCs replacing face-to-face or phone consultations was significantly associated with decreased use (aOR=0.47, 95% CI 0.26-0.87); Table 5. Figure 1 represents the weight of each factor on the use of OSCs. The main factor that significantly increased the probability of using OSCs was the tools’ ease of use (aOR=8.17, 95% CI 4.25-15.71). This was followed by feeling reassured by using the tool and having children. Users of OSCs also usually thought of OSCs as improving their healthcare choices, their health literacy and self-care capacity. Encouragement from friends and family limited healthcare access, better. being female. and trust in the triage and diagnostic accuracy. Demographic factors associated with a decreased odd of using OSCs included male gender and older age. Concerns regarding privacy and data security, as well as the risk of increased inequalities and loss of face-to-face consultations due to OSCs were also identified as reducing the likelihood of using these tools. Conclusions: Principal results This study investigated the factors influencing the use of OSC in the UK through a cross-sectional survey of community dwelling adults. Among the key findings, we observed that most participants (86%) had used a symptom checker at some point, with the NHS 111 platform being the most widely used (79%), followed by Healthline (22%). These tools were predominantly employed before seeking medical advice (95%) primarily to better understand symptoms and to determine the need for care. Interestingly, while most OSC non-users expressed a strong likelihood of future use, we observed varying concerns regarding the accuracy of information, safety, privacy and potential increase of inequalities. Older individuals (aged between 46-55, 56-65 and older than 65) showed significantly decreased odds of using OSC compared to the 18-25 age group independently from other variables. However, females exhibited higher odds of utilising these tools compared to males, and individuals with children under 16 years of age were more than three times more likely to use OSC compared to those who did not have children. Further, although trust in the accuracy of diagnoses and triage recommendations, as well as encouragement from family and friends, positively influenced use, concerns regarding privacy, health information security, inequalities and the potential displacement of traditional consultations were significantly associated with decreased use (Figure 1). No significant associations were found between the use of OSCs and encouragement from GPs to use these tools, concerns about the safety of relying solely on symptom checkers nor potential health risks. Study strengths and limitations This study examined a wide range of variables, including demographics, motivations, perceived effectiveness, reliability and concerns regarding OSC and associations with their use providing valuable insights into the complexities surrounding the wide scale adoption and diffusion of these tools in the contemporary setting. While supporting existing evidence on this topic, we identified additional factors associated with the use of OSCs, including the perceived family and friends support as well as having children. By employing regression models, we were also able to quantify the associations between the use of OSCs and a variety of factors, including both demographic factors and respondents’ perceptions regarding the usability, safety, accuracy and concerns associated with OSCs. Crucially, we identified significant predictors of use while controlling for potential confounders and enhancing the internal validity. This study's large, diverse and representative sample closely reflects the ethnic distribution of the UK population [24], which enhances the generalisability of the findings. However, this study is also has several limitations. Firstly, cross-sectional studies cannot establish causality and temporal relationships between the factors examined. Secondly, since the survey was only accessible online, it is likely that potentially eligible participants with limited access to internet and / or those less confident with digital technology were excluded and their views absent or at least under-reported. Lastly, this study relies on self-reported data, which may be subject to recall and social desirability biases. Participants' responses regarding their use patterns, preferences and adherence to recommendations may therefore not fully reflect their actual behaviours. Longitudinal studies are indicated to follow-up users over a longer time horizon to better understand how their interactions with OSC evolve and how it influences their health behaviours, healthcare utilisation and health outcomes. Comparison with existing literature The findings of this study are in line with prior research, including primary studies and reviews reporting on the socio-demographics of OSC users who tend to be young [6, 14, 15], women [6, 14-16] and with higher education levels [6, 14, 16]. Although having a chronic health condition or a disability was associated with greater use in a study by Meyer et al. [16], we did not find this association in this study’s sample. Regarding the motivations for using OSCs, a better understanding of the causes of symptoms has also been found to be the primary motivation among US users of the Isabel Symptom Checker [16], followed by support for deciding whether to seek care. The finding that users of OSCs tend to find these tools easy to use and helpful was corroborated by Meyer et al. [16] and Pairon et al. [6]. The strong correlation identified in this study highlights the importance of user-friendly interfaces in promoting the adoption of OSCs. The review by Pairon et al. [6] also emphasized that users value OSCs for their ability to support health-related decisions, especially in determining whether to seek medical care. The results of this study reinforce this by showing that individuals who perceived OSCs as helpful in making better medical care choices were nearly three times more likely to use them. Compliance with OSC recommendations has also been a point of focus in the literature. Previous studies reported varying levels of adherence to OSC advice, with compliance rates ranging from 57% to 67% [6], whereas this study reports a higher compliance rate, with 87.5% of participants following OSC recommendations most or all of the time. This higher rate of adherence may reflect an increasing reliance on digital health tools, particularly in the context of the COVID-19 pandemic which has accelerated the adoption of telemedicine and online health resources. Further research is needed to continue monitoring these trends and to explore the long-term impact of OSC use on healthcare outcomes. Finally, issues relating to perceived accessibility, accuracy, security and privacy of OSCs were also identified by Aboueid et al. [22] in their qualitative study exploring young adults’ perspectives on the use of OSCs. Most of their respondents thought of OSCs as more useful to self-triage than self-diagnosis, which reflects the fact that only 63% of the respondents in this study trusted the diagnosis provided by the OSC, compared to 79.2% for the triage suggestion. Implications for research Although this study identified demographic disparities in the use of OSCs, further research is warranted to understand the underlying reasons for these disparities. Research focusing on the socio-cultural factors, digital literacy and healthcare-seeking behaviours among different demographic groups could provide valuable insights into addressing disparities and promoting equitable access to OSC. In addition, this study’s findings highlight the importance of usability, effectiveness and trust in driving the adoption and utilisation of OSCs. Future research could investigate deeper into the specific features and functionalities of these tools, such as user interface design and decision support algorithms that contribute to their perceived usability, effectiveness and adherence to the recommendations. This study highlighted concerns regarding the privacy and health information security of OSC, which could impact their acceptance and use, necessitating the development of robust frameworks, regulatory standards and guidelines for OSC platforms to ensure transparency, accuracy and user privacy. Additionally, studies investigating the potential implications of OSC on healthcare inequalities and the doctor-patient relationship are essential for informing policy and practice, whereas research exploring effective strategies for educating users about the capabilities and limitations of these tools, as well as enhancing communication and collaboration between users and healthcare providers, could help build trust and confidence in OSC. Future research should focus on understanding the socio-cultural factors influencing OSC use and developing strategies to address privacy and security concerns. Additionally, efforts to improve the usability and reliability of OSCs, alongside targeted interventions to promote equitable access, are essential for integrating these tools effectively into the healthcare system. By addressing these issues, OSCs can play a key role in supporting self-care and improving healthcare accessibility and efficiency in the UK. Conclusion This study provides insights into the factors influencing the use of OSCs in the UK, highlighting both their increasing and widespread adoption and some of the concerns associated with these digital health tools. The findings indicate that most adults have used OSCs, particularly the NHS 111 service, primarily for understanding symptoms and determining the need for medical care, and that younger individuals, females and those with children are more likely to use OSCs overall. Ease of use, perceived helpfulness in medical decision-making, and trust in the accuracy of diagnoses and triage recommendations are key factors driving OSC use, but these are coupled to concerns about privacy, health information security and the potential for OSCs to exacerbate healthcare inequalities, posing significant barriers to their adoption. The fear of OSCs replacing traditional consultations with healthcare professionals remains common among users, and these concerns must be addressed to enhance user trust and maximize the benefits of OSCs in healthcare delivery.

  • Background: Many healthcare systems confront considerable strain attributable to an escalating prevalence of older adults living longer leading to an increased number of people with chronic conditions. Concomitantly the numbers of trained professionals in the healthcare workforce is not keeping up with the increased numbers of people with chronic conditions. In this context, increased digitalization is considered one way to mitigate many of the challenges, but it remains to be documented whether this is of benefit to COPD patients. The Epital Care Model (ECM) constitutes a proactive and data-centric treatment paradigm that leverages patient-reported outcome data and 24/7 telehealth service to facilitate early detection of deteriorating conditions among patients with chronic diseases (1). This approach aims to reduce and address exacerbations early, thereby averting the need for extensive and resource-intensive interventions. It is noteworthy that the Epital frontline service is delivered by trained and certified staff consisting of students from health educations and not by health care professionals. Objective: This clinical controlled trial was conducted to investigate the impact of the virtual component of the ECM framework in COPD on healthcare resource utilization and participants mental wellbeing and social activities. Methods: A pragmatic step-wedged design was employed, involving the random allocation of 184 patients into either an intervention group (n=92) or a control group (n=92), with equitable distribution across four general practice clinics in Denmark. Participants were examined at an 8-month (T1) follow-up and 12-month (T2) follow-up. Healthcare service utilisation and participants’ social activity were assessed and compared using Poisson regression. Mental wellbeing was assessed by comparing the scores on the WHO-5 wellbeing index using an unpaired t-test. Results: A significant reduction of healthcare utilization associated with COPD was found in the intervention group at T2, with reduced hospital admissions (56%), general practitioner visits (78%), on-call doctor consultations (73%), emergency room visits (49% reduction), and outpatient attendances (60% reduction) compared to the control group. Further, there was a significant increase in social activities (p< 0.01) and travel activities abroad (p< 0.01) at T2 in the intervention group, but no difference was found in well-being (WHO-5 index) between the two groups Conclusions: The study highlights the value of the ECM virtual care model in COPD management, offering a potential solution to healthcare workforce shortages and resource constraints as it leads to both a significantly reduced use of healthcare services and at the same time introduces a new kind of workforce to complement the existing workforce. Further research using this model in other chronic conditions and other healthcare systems is warranted based on these findings. Clinical Trial: No trial registration has been performed. The protocol is available from: https://epital.com/temokap-protokol-2/

  • Background: Patient-Generated Health Data (PGHD) has been recognized as a potential tool in transforming healthcare from clinician-centered to more patient-centered approaches. This transformation is driven by the potential of PGHD to provide deeper insights into patients' conditions, facilitate personalized care, improve patient quality of life, reduce inefficiencies in data collection, and empower patients. Yet, actual implementation within clinical settings is still at early stages, and therefore impacts on clinical care remain limited. Objective: This study sought to explore the benefits, challenges, and opportunities of integrating PGHD into orthopaedic care by analyzing the reflections of early adopter surgeons and physiotherapists, who have used a digital care management platform. Methods: This qualitative study employed thematic analysis of interviews conducted with surgeons and physiotherapists (n=9) from an early adopter unit using "mymobility" an industry produced software platform. The participants were recruited using snowball sampling, and interviews were conducted from June to July 2022. The interviews focused on current work practices, use of digital tools, experiences with PGHD, and experiences with the mymobility software. Thematic analysis was conducted using NVivo software, focusing on identifying key themes and insights Results: The study identified several benefits of integrating PGHD into orthopaedic care, including improved patient education, enhanced communication and assessment, and increased patient motivation and adherence. However, several challenges were also noted, such as increased clinician workload, questionable data utility, lack of patient centricity, and inability to tailor software to clinical contexts. Suggested opportunities included improving dashboard design, personalizing physiotherapy, and using collected data for improving clinical care. Conclusions: The integration of PGHD into orthopaedic care shows promise, largely in areas suggested by literature. However, significant challenges remain. Future research should focus on addressing solvable challenges such as improving software user interface design and functionality, while embracing the possibility that some challenges lack clear solutions and will likely require careful balancing of design tensions. The findings highlight the need for ongoing development and refinement of PGHD-inclusive systems to better support clinical practice and patient outcomes.

  • Background: Race/ethnicity and gender concordance between patients and providers is a potential strategy to improve healthcare interventions. However, findings have been conflicting for patient experiences and outcomes, with the impact in physical therapy, especially in a digital context, being largely unexplored. Objective: To evaluate the impact of race/ethnicity or gender concordance between patient and physical therapist in engagement and clinical outcomes following a digital care program in patients with musculoskeletal conditions. Methods: This secondary analysis of two prospective longitudinal studies examined the impact of both race/ethnicity and gender concordance between patients and physical therapists on outcomes for a digital intervention for musculoskeletal conditions. Outcomes included engagement (measured by completion rate), satisfaction and clinical outcomes (response rate for pain, anxiety, depression, and daily activities impairment). Results: From 71,201 patients, 63.9% (N=45,507) were matched with their physical therapist in terms of race/ethnicity, while 61.2% (N=43,560) were matched for gender. Concordant dyads showed a higher completion rate among White (adjusted OR 0.90, 95% CI 0.84;0.95, P<.001) and Hispanic (adjusted OR 0.79, 95% CI 0.65;0.93, P=.009) groups, as well as women (adjusted OR 0.90, 95% CI 0.85;0.94, P<.001). Concordance did not affect satisfaction, with high values (>8.52, 95%CI 8.27;8.77) reported across all dyads except in Asian patients, who experienced higher satisfaction in discordant dyads. Response rates for pain, anxiety and daily activities impairment were unaffected by race/ethnicity concordance. An exception was observed for depression, with White patients reporting higher response rate when matched with physical therapists from other races/ethnicities (adjusted OR 1.20, 95% CI 1.02;1.39, P=.02). In terms of gender, men had a slightly higher pain response in discordant dyads (adjusted OR 1.08, 95% CI 1.01;1.15, P=.03), and higher depression response in concordant dyads (adjusted OR 0.81, 95% CI 0.68;0.95, P=.01). Conclusions: Race/ethnicity and gender concordance between patients and physical therapists did not translate into higher satisfaction or improvement for most clinical outcomes, aside from a positive effect on adherence. These results highlight the importance of other physical therapists characteristics besides race/ethnicity or gender concordance, suggesting the potential benefit of experience, languages spoken and cultural safety training as ways to optimize care. Clinical Trial: ClinicalTrials.gov (NCT04092946, NCT05417685)

  • The Sentiment of AI-Assisted Writing Among Editors of the Top 200 Medical Journals: A Survey-Based Analysis

    Date Submitted: Aug 11, 2024
    Open Peer Review Period: Aug 11, 2024 - Oct 6, 2024

    Background: Artificial intelligence (AI) tools are increasingly used to respond to user questions on demand, offering prospective researchers efficiency and productivity. Despite these benefits, the use of AI in academic writing remains controversial. This survey-based study analyzes the opinions of editors from the top 200 medical journals on the use of AI in academic writing. Objective: To analyze the opinions of editors from the top 200 medical journals on the use of artificial intelligence (AI) in academic writing, assessing its acceptance, required disclosure, and acceptable levels of AI-generated content. Methods: Editors from the top 200 medical journals on Scimago were polled about their opinions on AI in academic writing. Emails of 491 editors from 145 journals were manually collected and contacted. A custom, unverified survey with five questions was sent via Qualtrics on August 1st, 2023, and re-emailed monthly for three months. The survey covered AI-assisted publications, AI disclosure, the impact of AI on efficiency, and acceptable AI usage in writing. Responses from 21 editors across 19 journals were manually recorded and analyzed, including journal impact factors, H-Index, and country of publication. Results: A total of 21 editors from 19 journals responded. Among them, 86% would accept publications with AI involvement. A significant majority (95%) agreed that AI usage should be disclosed in manuscripts. Opinions on acceptable AI-generated content varied: 38.1% felt the threshold did not matter, while others believed it should range from less than 10% to 100%. The surveyed journals had an average impact factor of 15.55 and an average H-index of 365.5. Conclusions: AI is generally perceived positively and can streamline the writing process, promoting equal opportunities for researchers with varying writing proficiencies. It is recommended that AI usage in writing be capped at 10-25%, with discretion for 25-50% usage, and papers exceeding 50% AI content should be denied. Enhanced AI detection capabilities are anticipated to aid in this process. Clinical Trial: n/a

  • Background: Background Acute pancreatitis is a primarily sterile inflammation caused by premature intracellular protease activation, which has caught the attention of social media platforms such as Bilibili, TikTok, and YouTube. However, the content and quality of medical information on social media exists unclear and indeterminate. Objective: The purpose of the study is to evaluate the content and quality of online videos about acute pancreatitis from Bilibili, TikTok, and YouTube Methods: Methods A video search using acute pancreatic-related keywords was conducted on three video-sharing platforms: Bilibili, TikTok, and YouTube. We recorded basic information presented in the videos and identified the source and content type of each video. The educational content and quality of each video were evaluated using the Global Quality Scale (GQS), Journal of the American Medical Association (JAMA), and Modified DISCERN. A comparative analysis was conducted on the videos obtained from these three sources. Results: Results 300 videos were considered for assessment. Most videos were provided by health professionals (50.7%, 152/300), followed by nonprofit organizations (27.7%, 83/300). Additionally, 13.2% of videos (36/300) were offered by science communicators, and 5.7% (17/300) were provided by general users. The remaining videos were uploaded by news agencies (3.3%, 10/300) and two for-profit organizations (0.7%, 2/300). The content types of the 300 videos were classified into five categories: clinical diagnosis (25%, 75/300), prognosis (5%, 15/300), etiologies and causations (6.7%, 20/300), scientific introductions (51%, 153/300), and treatment methods (12.3%, 37/300). The overall quality of the videos, as evaluated by GQS, JAMA, and Modified DISCERN, was found to be moderate, with scores of 2.67/5, 2.22/4, and 2.63/5 points, respectively. Conclusions: Conclusions Video-sharing platforms have become easily accessible sources for patients seeking information about their diseases. This innovative study demonstrates that social media videos can facilitate public learning about clinical diagnosis, prognosis, treatment methods, etiologies and causations, and scientific introductions of acute pancreatitis. However, both the content and quality of uploaded videos are currently inadequate. In the future, greater efforts should be made to enhance the content and quality of videos on acute pancreatitis and increase public awareness.

  • Background: Kidney diseases encompass a variety of conditions, including chronic kidney disease, acute kidney injury, glomerulonephritis, and polycystic kidney disease. These diseases significantly impact patients' quality of life and healthcare costs, often necessitating substantial lifestyle changes, especially regarding dietary management. However, patients frequently receive ambiguous or conflicting dietary advice from healthcare providers, leading them to seek information and support from online health communities. Objective: This study aims to analyze social media data to better understand the experiences, challenges, and concerns of kidney disease patients and their caregivers in South Korea. Specifically, it explores how online communities assist in disease management and examines the sentiment surrounding dietary management. Methods: Data were collected from "KidneyCafe," a prominent South Korean online community for kidney disease patients hosted on the Naver platform. A total of 124,211 posts from ten disease-specific boards were analyzed using latent Dirichlet allocation for topic modeling and Bidirectional Encoder Representations from Transformers (BERT)-based sentiment analysis. Additionally, efficiently learning an encoder that classifies token replacements accurately (ELECTRA)-based classification was used to analyze posts related to disease management further. Results: The analysis identified six main topics within the community: Family Health and Support, Medication and Side Effects, Examination and Diagnosis, Disease Management, Surgery for Dialysis, and Costs and Insurance. Sentiment analysis revealed that posts related to Medication and Side Effects topic and Surgery for Dialysis topic predominantly expressed negative sentiments. Both significant negative sentiments concerning worries about kidney transplantation among family members and positive sentiments regarding physical improvements post-transplantation were expressed in posts about family health and support. For Disease Management, seven key subtopics were identified, with inquiries about dietary management being the leading topic. Conclusions: The findings highlight the critical role of online communities in providing support and information for kidney disease patients and their caregivers. The insight gained from this study can inform healthcare providers, policymakers, and support organizations to better address the needs of kidney disease patients, particularly in areas related to dietary management and emotional support.

  • A Systematic Review of Digital Health Solutions for Cardiovascular Disease Prevention

    Date Submitted: Aug 2, 2024
    Open Peer Review Period: Aug 8, 2024 - Oct 3, 2024

    Background: Background: Cardiovascular disease (CVD) remains a critical global health concern, contributing significantly to global mortality rates, with approximately 70% of cases linked to modifiable risk factors. While traditional methods for CVD prevention face challenges, digital health technologies offer promising avenues for intervention. However, the extent to which these technologies comprehensively address the spectrum of CVD prevention strategies remains unclear. Currently, there is an absence of systematic evaluations within the existing literature regarding the effectiveness and scope of digital solutions in CVD prevention. This emphasizes the need for examination and synthesis of available research to guide future developments and implementations. Objective: Objective: This review aims to make a thorough analysis of how digital technologies can effectively tackle the challenges posed by traditional approaches to CVD prevention. This review aims to consolidate existing literature on digital solutions for CVD prevention, delineate the key components of successful CVD prevention targeted by digital solutions, and outline the research gaps requiring attention to foster the development of sustainable and scalable digital solutions for CVD prevention. Methods: Methods: Our methodology involved identifying primary literature on CVD prevention using digital solutions, specifically technologies facilitating remote care beyond traditional telephone use. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework as a guideline, we searched comprehensively in Web of Science, Scopus, and PubMed to retrieve original studies published in English between January 2000 and May 2024. Results: Results: Our search identified 30 eligible studies, with 24 (80%) being randomized controlled trials (RCTs). The digital solutions reviewed for CVD prevention primarily focused on baseline assessment (97%,), physical activity counseling (60%), tobacco cessation (47%), blood pressure management (43%), and medication adherence (33%). Technologies such as smartphones and wearables (53%), email and short message service (SMS) communications (40%), and websites or web portals (10%) were utilized in the studies. Approximately half of the studies addressed blood pressure, exercise capacity, and weight, whereas less than a third addressed medication use, quality of life, dietary habits, intervention adherence, waist circumference, and blood glucose levels. Conclusions: Conclusions: Digital solution has the potential to alleviate challenges associated with traditional CVD prevention approaches by enhancing preventive behaviors and monitoring health indicators. However, evaluated interventions have predominantly focused on medication use, quality of life, dietary habits, intervention adherence, and waist. Thus, subsequent studies are crucial with more extensive interventions in CVD prevention for assessing their lasting effect on key cardiovascular outcomes. Clinical Trial: -

  • City-Cut or Radius-Cut? Design Principle of the Chinese Anti-pandemic Traveling Record Card System

    Date Submitted: Jul 31, 2024
    Open Peer Review Period: Aug 8, 2024 - Oct 3, 2024

    Analysis of 25 million Shanghai citizens’ daily movements supports the city-cut approach for travel restrictions in the early stage of the COVID-19 pandemic. The city’s high connectivity and compact nature justify the city-cut approach over the geographic distance-based radius-cut approach, effectively containing the spread of COVID-19 within the metropolis.

  • Background: Telepathology has proven to be a viable solution to provide timely, high-quality diagnostic services to underserviced, remote areas, and is widely applied in the word. The government strongly supports the development of telepathology to alleviate the shortage of pathologists in China. This study aimed to survey telepathology in China, and analyze the usage and attitudes of both pathologists and patient. Objective: This study aims to analyze the construction, application, and development of telepathology in China from 2018 to 2023, understand the usage, evaluation, and attitudes of pathologists and patients towards telepathology, and analyze the existing problems and improvement suggestions in the application of telepathology. Methods: A national survey was administered to Chinese hospitals in 2018, 2019, 2020 and 2023. A survey to doctors and patients who participated in telemedicine services was conducted in 2019. Based on this data, we analyze telepathology in China. Chi-square test and Fisher's exact probability test were used to test the difference among different years, different levels of hospitals, and different regions. Results: The annual average growth rate of the proportion of telepathology software in the surveyed hospitals was 7.60%, from 29.53% in 2018 to 42.55% in 2023. From 2018 to 2023, the number of telepathology service cases in China has been continuously increasing, with the median number per hospital growing from 51 to 200 cases, and the fastest growth was in the eastern region. Once a week was the dominant frequency(63.64%) among pathologists in China. The average time for participating in telepathology services was mainly 11-20 min and 21-30min. The pathologists' overall satisfaction rate was 99.35%, and the most frequently mentioned difficulties were unreasonable scheduling, small coverage, inadequate publicity, and network problems. 92.20% of the patients received their diagnostic results within 24 hours, all patients were satisfied with telepathology, and 99.29% of them were willing to recommend it to other patients. Conclusions: The construction and application of telepathology in China have achieved significant growth from 2018 to 2023. Despite some existing barriers with its application, both pathologists and patients exhibit high satisfaction with telepathology. Unreasonable scheduling, limited coverage, inadequate publicity, and network issues are the main problems affecting telepathology. Recommendations include expanding service methods, shortening wait times, strengthening publicity, incorporating telepathology fees into medical insurance, and enhancing training for primary healthcare personnel.

  • Background: Artificial intelligence (AI)-based clinical decision support systems are increasingly used in healthcare. Uncertainty-aware AI presents the model’s confidence in its decision alongside its prediction whereas black-box AI only provides a prediction. Little is known about how this type of AI affects healthcare providers’ work performance and reaction time. Objective: To determine the effects of black-box and uncertainty-aware AI advice on pharmacist decision-making and reaction time. Methods: Thirty licensed pharmacists participated in a crossover, randomized controlled trial. Eligible participants were randomized to either the black-box AI or uncertainty-aware AI condition in a 1:1 manner. Participants completed 100 mock verification tasks with AI help and 100 without AI help. The order of no help and AI help was randomized. Participants were exposed to correct and incorrect prescription fills, where the correct decision was to ‘accept’ or ‘reject’, respectively. AI help provided correct (79%) or incorrect (21%) advice. Reaction times, participant decision, AI advice, and AI help type were recorded for each verification. Likelihood ratio tests (LRT) compared means across the three categories of AI type for each level of AI correctness. Results: Participants’ decision-making performance and reaction times differed across the three conditions. Accurate AI recommendations resulted in the rejection of the incorrect drug 96.1% and 91.8% of the time for uncertainty-aware AI and black-box AI respectively, compared to 81.2% without AI help. Correctly dispensed medications were accepted at rates of 99.2% with black-box help, 94.1% with uncertainty-aware AI help, and 94.6% without AI help. Uncertainty-aware AI protected against bad AI advice to approve an incorrectly filled medication compared to black-box AI (83.3% vs 76.7%). When the AI recommended rejecting a correctly filled medication, pharmacists without AI help had a higher rate of correctly accepting the medication (94.6%) compared to uncertainty-aware AI help (86.2%) and black-box AI help (81.2%). Uncertainty-aware AI resulted in shorter reaction times than black-box AI and no AI help except in the scenario where "AI rejects the correct drug". Black-box AI did not lead to reduced reaction times compared to pharmacists acting alone. Conclusions: Pharmacists’ performance and reaction times varied by AI type and AI accuracy. Overall, uncertainty-aware AI resulted in faster decision-making and acted as a safeguard against bad AI advice to approve a misfilled medication. Conversely, black-box AI had the longest reaction times, and user performance degraded in the presence of bad AI advice. However, uncertainty-aware AI could result in unnecessary double-checks, but it is preferred over false negative advice, where patients receive the wrong medication. These results highlight the importance of well-designed AI that addresses users’ needs, enhances performance, and avoids overreliance on AI.

  • Background: Recent advancements in artificial intelligence (AI), such as ChatGPT, have demonstrated significant potential by achieving good scores on text-only United States Medical Licensing Examination (USMLE) exams and effectively answering questions from physicians. But its ability to interpret images is not well studied. Objective: Recent advancements in artificial intelligence (AI), such as ChatGPT, have demonstrated significant potential by achieving good scores on text-only United States Medical Licensing Examination (USMLE) exams and effectively answering questions from physicians. But its ability to interpret images is not well studied. Methods: We used multiple-choice questions with images from the USMLE to test GPT-4V’s accuracy and explanation quality. GPT-4V's accuracy was compared to two state-of-the-art LLMs, ChatGPT and GPT-4. The quality of explanations was evaluated across 3 qualitative metrics: comprehensive explanation, question information, and image interpretation. To better understand GPT-4V’s explanation ability, we modified a patient case report to resemble a typical “curbside consultation” between physicians. Results: GPT-4V outperformed ChatGPT (58.4%) and GPT-4 (83.6%) with an overall accuracy of 90.7%. For questions with images, GPT-4V achieved an accuracy of 62.0%, equivalent to the 70th-80th percentile among medical students. When GPT-4V answered correctly, its explanations were nearly as good as those provided by domain experts. However, incorrect answers often had poor explanation quality: 18.2% contained fabricated text, 45.5% had inferencing errors, and 76.3% demonstrated image misunderstandings. With human expert assistance, GPT-4V reduced errors by an average of 40.5%. Nevertheless, in curbside consult setting, GPT-4V required continuous specialized guidance to make partially correct diagnoses and subsequent examination recommendations. Conclusions: GPT-4V achieved high accuracy on multiple-choice questions with images. However, the explanation quality was poor when answered incorrectly, and this issue could not be efficiently resolved through expert interaction in clinical practice. Our findings highlight the need for more comprehensive evaluations beyond multiple-choice questions before integrating GPT-4V into clinical settings.

  • Digital tools for people in pre-dementia stages: a scoping review

    Date Submitted: Jul 29, 2024
    Open Peer Review Period: Aug 6, 2024 - Oct 1, 2024

    Background: The field of Alzheimer’s disease (AD) moves towards earlier diagnoses in pre-dementia stages, personalized prognosis, and dementia prevention. In the near future, a gap is expected between the growing demand for Alzheimer-related healthcare and a shrinking workforce. Responsibility is increasingly assigned to individuals to take an active role in their own brain health. Digital tools are thought to offer support with regard to these processes. Objective: The aim of this scoping review is to create an overview of digital tools published in scientific literature in the context of Alzheimer’s disease and dementia, with cognitively unimpaired people and/or people in pre-dementia stages as primary end-users interacting with these digital tools. Additionally, we aim to gain insight into study sample diversity, the stage of maturity and evaluation of these tools, and recommended future directions. Methods: PubMed, IEEE Xplore, Ovid, and Web of Science were searched in January 2023, using terms on Alzheimer’s disease and dementia, (pre-)disease stages, digital tools, and purposes of digital tools. Two independent reviewers screened 2811 records on title and abstract, and subsequently 408 full text articles, based on in- and exclusion criteria. Articles on tools targeting those with an Alzheimer’s disease or dementia diagnosis were excluded. Data extraction included information on the sample characteristics, the digital tool, stage of maturity and evaluation, and future (research) directions. Results: We included 39 articles, which were aimed at primary prevention (15/39; 38.5%), secondary prevention (10/39;25.6%), daily life support (8/39; 20.5%), diagnosis and risk assessment (4/39;10.3%), or decision-making (2/39; 5.1%). Variation in study sample emerged regarding cognitive abilities (healthy (11/39; 28.2%); mild cognitive impairment (11/39; 28.2%), (subjective) cognitive impairment (10/39; 25.6%); ‘no dementia’ (1/39; 2.6%), and variation of cognitive abilities (6/39; 15.4%)). Less variation was found regarding sex (>50% female: 27/39; 69.2%), education ( >50% high education: 13/39; 33.3%), and age (>50% >60 years: 23/39; 59%). Few articles reported on ethnicity (12/39; 30.7%) and digital literacy (11/39; 28.2%). Most tools were in an early evaluation and maturity stage (31/39; 79.5%), comprising pre-prototyping (1/35; 2.9%), prototyping (15/35; 42.9%), pilot testing (19/35; 54.3%), efficacy testing (18/40; 45%), usability testing (12/40; 30%), and feasibility testing (10/40; 25%). Future (research) directions comprise the need for further tool development, attention to diversity, and study advancements, such as large-scale longitudinal studies. Conclusions: 79.5% of tools as reported on in academic literature are considered to be in an early maturity stage. Studies and evidence gathered for digital tools for people (at risk) in pre-dementia stages is thus preliminary and further developments and research is needed before these tools can be implemented for assessing, supporting and preventing cognitive decline.

  • Background: To date, no studies have examined adherence to the 2018 Physical Activity Guidelines for Americans (PAGA) in real-world longitudinal settings using objectively measured activity monitoring data. This study addresses this gap by utilizing commercial activity monitoring (Fitbit) data from the All of Us (AoU) dataset. Objective: The primary objectives were to describe the prevalence of adherence to the 2018 PAGA and identify associated socio-demographic determinants. Additionally, we compared three distinct methods of processing physical activity data to estimate adherence to the 2008 PAGA. Methods: We used the National Institutes of Health (NIH) AoU dataset, which contains minute-level Fitbit data for 13,947 US adults over a seven-year time span (2015 to 2022), to estimate adherence to PAGA. We used a published step-based method to estimate metabolic equivalents (METs) and assess adherence to 2018 PAGA (i.e., ≥ 150 minutes of moderate to vigorous intensity physical activity per week). We compared the step-based method, a heart rate (HR) based method and the proprietary Fitbit-developed algorithm to estimate adherence to the 2008 PAGA. Results: The average overall adherence to 2018 PAGA was 21.6% (SE=±0.4%). Factors associated with lower adherence in multivariate logistic regression analysis included female sex (relative to male sex; adjusted odds ratio [AOR] = 0.66; 95% CI: 0.60 – 0.72; P < .0001), BMI of 25.0 – 29.9, 30 – 34.9, or ≥ 35 (relative to a BMI of 18.5 – 24.9; AOR = 0.53, = 0.30, = 0.13; 95% CI: 0.46 – 0.60, 0.25 – 0.36, 0.10 – 0.16, P < .0001, <.0001, < .0001 respectively), being aged 30 – 39, 40 – 49, or aged ≥ 70 (relative to being 18 – 29 years old; AOR = 0.66, = 0.79, = 0.74; 95% CI: 0.56 – 0.77, 0.68 – 0.93, 0.62 – 0.87; P < .0001, = .0052, = .0004 respectively), and non-Hispanic Black race/ethnicity (relative to non-Hispanic White race/ethnicity; AOR = 0.63; 95% CI: 0.50 – 0.79; P = .0001). The Fitbit algorithm estimated that a larger percentage of the sample (73.9%, 95% CI: 71.2–76.6) adhered to the 2008 PAGA compared to the HR method estimate (34.0%, 95% CI: 32.8–35.2) and the step-based method (10.0%, 95% CI: 9.4–10.6). Conclusions: Our results show significant sociodemographic differences in PAGA adherence, and notably different estimates of adherence depending on the algorithm used. These findings warrant the need to account for these disparities when implementing physical activity interventions, and the need to establish an accurate and reliable method of using commercial accelerometers to examine physical activity, particularly as healthcare systems begin integrating wearable device data into patient health records.

  • Background: The COVID-19 pandemic forced the world to quarantine to slow the rate of transmission, causing communities to transition into virtual space. Asian American and Pacific Islander (AAPI) communities faced the additional challenge of discrimination that stemmed from racist and xenophobic rhetoric in the media. Limited data exists about technology usage during the height of the COVID-19 shelter-in-place among AAPI adults and its effect on their physical and mental health. Objective: This study aims to examine AAPI adults’ perspectives and experiences about their use of technology during the COVID-19 pandemic. Methods: We leveraged community partners and social media to distribute the COVID-19 Effects on the Mental and Physical Health of AAPI Survey Study (COMPASS), a nationwide multi-lingual survey available in English, Chinese, Korean, Samoan, and Vietnamese. The survey was administered from October 2020–February 2021 and participants rated their level of agreement (1=Not at all to 5=Extremely) on six items about their attitudes toward using technology. Thematic analysis was conducted to an open-ended question: “Is there anything else you want to tell us about your use of technology during COVID-19?” The qualitative responses were reviewed, analyzed, organized into various codes, and then categorized into corresponding themes. Results: The mean age of respondents was 45.9 years old (range 18-98) with 5,398 participants of the quantitative survey and 1,115 unique responses to the open-ended question. In the quantitative survey, 68% of respondents said they were comfortable using technology, with the majority saying it helps them keep up with news (80%), social connections (76%), and care for others (47%). However, it showed mixed opinions on how useful technology was for health: 40% agreed it is helpful for mental health but disagreed for physical health. Four main themes from the qualitative analysis were derived: (a) Technology was critical to function in many aspects of life and maintain physical, mental, and emotional well-being; (b) Technology was often the only source for interpersonal social connections; (c) Overuse led to negative physical & mental health; and (d) Technology use was associated with multiple challenges and barriers. Conclusions: Findings revealed diverse perspectives and experiences in technology use for AAPI adults during the height of the COVID-19 pandemic. Dependence on technology may have exacerbated social inequities with a lack of access to devices and Wi-Fi to maintain work, interview for new jobs, and communicate virtually for persons who have limited English proficiency. Further qualitative research would be beneficial in amplifying AAPI perspectives to uncover concerns and address health disparities.

  • The Impact of Tele-ICU in Clinical Outcomes of Critically Ill Covid-19 Patients in Brazil

    Date Submitted: Aug 1, 2024
    Open Peer Review Period: Aug 1, 2024 - Sep 26, 2024

    Background: In Brazil, the high demand for intensive care unit (ICU) beds during COVID-19 pan-demic caused great social impact. The lack of trained professionals, especially in-tensivists, impelled government and specialists to seek for alternatives to deliver efficient care. In this scenario, the Tele-ICU COVID-19 Brazil program was created to provide remote intensivists to guide daily multidisciplinary rounds (DMRs) in pub-lic COVID-19 ICUs from the country. Objective: The objective of the present study was to evaluate the association of the adherence to the Tele-ICU COVID-19 Brazil Program and clinical outcomes of patients with COVID-19. Methods: Retrospective study with all the ICUs participants of the Tele-ICU COVID-19 Brazil program. In order to assess the Tele-ICU impact on clinical outcomes, we analyzed the adherence to the program at the patient level, the DMR patient ratio (defined as number of DMRs for each patient divided by patient’s ICU length of stay); at the ICU level, the DMR ICU ratio (defined as DMR-days divided by patient-days) and each ICU’s length of participation in the project. We made comparisons between the groups: Low DMR (〖"DMR" 〗_"patient" " ratio" < 50%) vs high DMR (〖"DMR" 〗_"patient" " ratio" > 50%) and low engagement (〖"DMR" 〗_"patient" " ratio" < 50%) vs high engagement (〖"DMR" 〗_"ICU " "ratio" > 50%). Additionally, we performed multivariate analyses using multilevel mixed modelling to evaluate independent predictors of ICU mortality and ICU length of stay. Results: 1680 patients were included in the study. Compared to the low DMR group, patients from the high DMR group had shorter ICU [6(3-11) vs 11(6-20) days; p<0.001] and shorter hospital length of stay [9(5-16) vs 14(8-26) days; p<0.001]. However, the SOFA score was higher in the low DMR group [3(0-6) vs 2(0-5) days; p=0.007]. Compared to the low engagement group, patients from the high engagement group, had lower SOFA score [2(0-4) vs 3(1-7) days; p<0.001], lower ICU mortality (45.3% vs 52.2%; p=0.004), shorter ICU length of stay [7 days (4-12) vs 9(5-17) days; p<0.001] and shorter hospital length of stay [8(5-16) vs 14(7-23) days; p<0.001]. The multivariate analysis identified the use of mechanical ventilation (MV), non-invasive ventilation (NIV) and vasopressor as that independently predictors of ICU mortality. In contrast, a higher DMR per patient ratio was found to be a protective factor, associated with a decreased mortality risk (OR: 0.52; 95%CI: 0.27-0.99; p=0.048). Predictors for a longer ICU length of stay were higher SOFA score and the use of MV, while a higher DMR per patient ratio was found to be a protective factor, associated with a shorter ICU stay (OR: 0.17; 95%CI: 0.13-0.21; p<0.001). Conclusions: During Tele-ICU COVID-19 Brazil program, patients who received DMRs more fre-quent had lower risk for ICU mortality and shorter ICU length of stays.

  • Implementing Patient Accessible Electronic Health Records in Primary Care: Potential Barriers and Facilitators from Healthcare Professionals’ Perspective

    Date Submitted: Aug 1, 2024
    Open Peer Review Period: Aug 1, 2024 - Sep 26, 2024

    Background: Patients are increasingly being offered online record access (ORA) through Patient Accessible Electronic Health Records (PAEHR), yet implementation is often met with resistance from healthcare professionals (HCPs). Experiences from previous implementations may provide important insights into potential barriers and facilitators. Objective: To investigate factors influencing implementation of the Swedish PAEHR in primary care from HCPs’ perspective. Methods: We conducted 14 semi-structured interviews with a diverse group of HCPs shortly after the implementation of the Swedish PAEHR. The interviews were analysed using the Consolidated Framework for Implementation Research (CFIR) and content analysis, identifying key themes related to PAEHR implementation. Results: Several potential barriers and facilitators were identified. According to HCPs, the PAEHR had shortcomings but offered some flexibility. HCPs described working in a complex and challenging organisation, which nonetheless had an existing structure, support, and established communication with patients. They also described using EHR system for other purposes than documentation. Moreover, they reported dealing with a complicated patient group with varying needs and high expectations. HCPs expressed that they worked in a patient-centred way and with patient engagement. The HCPs could see both advantages and disadvantages with PAEHR and had some concerns. There were mixed views of the extent of the change, where some felt patient ORA would not affect their work at all and others expected a substantial impact. Some had experiences using PAEHR themselves, while some lacked knowledge or interest. Furthermore, the implementation process was perceived as long and uneventful, with fragmented communication, where existing communication activities were utilized. They also reported receiving some information and education about PAEHR outside the organisation. HCPs had limited awareness of how patients were introduced to the PAEHR. Conclusions: Shortcomings with the EHR system and within the organisation must be addressed for optimal implementation where patients’ and HCPs’ benefits and risks are mitigated. Moreover, improved information and education for patients and HCPs is a potential solution to address many concerns and perceived disadvantages of PAEHR.

  • Background: Older adults with cognitive deficits face difficulties recalling daily obstacles and lack self-awareness, amplifying the challenges for homecare clinicians to obtain reliable information on functional decline and homecare needs. The result may be suboptimal service delivery. Telemonitoring of ADL has emerged as a tool to optimize ADL homecare needs evaluation. Utilizing ambient sensors, telemonitoring of ADL gathers information about an individual's ADL behaviors within the home, such as preparing meals and sleeping. However, there is a significant gap in the comprehension of how ADL telemonitoring data can be integrated into clinical reasoning to better target homecare services. Objective: The current paper aimed to describe 1) how ADL telemonitoring data is used by clinicians in the process of maintaining care recipients with cognitive deficits at home as well as 2) the impact of ADL telemonitoring on homecare service delivery. Methods: We used an embedded mixed-methods multiple-case study design in which our cases of interest were three health institutions located in the greater Montreal region and offering public homecare services. An ADL telemonitoring system, named NEARS-SAPA, was deployed within those three health institutions for 4 years. Within each case were embedded sub-cases (care recipient, informal caregiver, clinician(s)). For the objectives of the present paper, we used the data collected during 45-60 min interviews with clinicians only. Quantitative metadata were also collected on each service provided to care recipients before and after the implementation of NEARS-SAPA to triangulate the qualitative data. Results: We analyzed 27 sub-cases, comprising 23 clinicians, that completed a total of 57 post-implementation interviews concerning 147 telemonitoring reports. Data analysis showed a 4-step decision-making process used by clinicians 1) Extraction of relevant telemonitoring data, 2) Comparison of telemonitoring data with other sources of information, 3) Risk assessment of the care recipient’s ADL performance and ability to remain at home, and 4) Maintenance or modification of the intervention plan. Quantitative data reporting the number of services received allowed to triangulate qualitative data pertaining to step 4. Overall, the results suggest a stabilization in monthly services following the introduction of the ADL telemonitoring system, particularly in cases where services were increasing prior to its implementation. This is consistent with qualitative data indicating that, in light of the telemonitoring data, most HSCP decided to maintain the current intervention plan rather than increasing or reducing services. Conclusions: Results suggest that ADL telemonitoring contributed to service optimization on a case-to-case basis. ADL telemonitoring may have an important role in reassuring clinicians about their risk management and the appropriateness of services delivery, especially when questions remain as to the relevance of services. Future studies may further explore the benefits of ADL telemonitoring for public healthcare systems with larger-scale implementation studies.

  • Background: Estimates indicate that more than one in eight people live with a mental health disorder, yet less than half of those suffering receive treatment. The prevalence of mental health disorders along with low rates of seeking treatment may be due to poor mental health awareness. Digital health technologies, like wearables and their associated phone- and web-based applications, have the potential to reduce the mental health awareness gap due to their ease of adoption, objective feedback, and high rate of engagement. Objective: To characterize the relationships between mental health and objective wearable-derived measures. Methods: We examined the longitudinal results of monthly mental health surveys (Patient Health Questionnaire-2, Generalized Anxiety Disorder 2-item, and Perceived Stress Scale) delivered over 13 months to 181,574 individuals wearing a device (WHOOP Inc., Boston, MA) that measures sleep, cardiorespiratory parameters, and physical activity (up to 307,860 survey responses and 7,942,176 days of total wear time). We utilized generalized linear mixed models and intrapersonal scaling to assess interpersonal and intrapersonal associations between wearable-derived metrics and mental health outcomes. Results: Results revealed that mental health outcomes improve with age, are better in males than in females, and are at healthier levels for those within the healthy or overweight range of body mass index relative to those in the underweight or obese range. Interpersonal associations between wearable-derived metrics and mental health outcomes indicate that individuals with better sleep characteristics (i.e., longer sleep durations and more consistent wake and sleep times), higher heart rate variabilities (HRV) and lower resting heart rates (RHR), and higher levels of physical activity report lower levels of depression, anxiety, and stress. Intrapersonal associations between wearable-derived metrics and mental health outcomes displayed similar results as the between-person analyses, with higher HRVs, lower RHRs, and more physical activity generally coinciding with improved mental health outcomes. However, intrapersonal wearable-derived sleep metric associations diverged from the interpersonal association findings when specifically looking at sleep duration and depression, whereby increased levels of sleep within an individual were associated with higher levels of depression. Conclusions: These results support a role for monitoring physiology and behaviors via wearables in complementing mental healthcare, as they have the potential to be used to track and optimize sleep behavior, cardiorespiratory physiology, and physical activity.

  • Background: Managing preoperative anxiety in pediatric anesthesia is challenging, as it impacts patient cooperation and postoperative outcomes. Both pharmacological interventions and non-pharmacological interventions are used to reduce children’s anxiety levels. However, the optimal approach remains debated, with evidence-based guidelines still lacking. As a consequence, many different approaches exist. Objective: To increase understanding of the current anxiety management practices, we conducted a public survey via social media platforms, aiming to compare anesthesia providers from an “expert” group and a “social media” group in terms of pediatric anesthesia expertise and to identify differences in preoperative anxiety management between the two groups. Methods: Two surveys were conducted: The first survey targeted attendees of the Scientific Working Group on Pediatric Anesthesia in June 2023 forming the ‘Expert Group’ (EG), and the second survey targeted followers of a pediatric anesthesia platform on social media forming the ‘Social Media Group’ (SG). Both surveys with 24 items were conducted using the same online platform. Questions were grouped into five categories: Pediatric Anesthesia Expertise, Representativity, Structural Conditions, Practices of Pharmacological Management and Practices in Non-Pharmacological Management. The primary objective was to assess the pediatric anesthesia expertise of the SG compared to the EG. Secondary objectives were the differences in the clustered categories with regards to preoperative anxiety management. Results: The study included 198 respondents, with 194 analyzed after excluding 4 due to prior participation or missing data (82 in EG and 112 in SG). The EG cohort exhibited significantly greater professional experience in pediatric anesthesia than the SG cohort (median 19 vs. 10 years, p<0.001), higher specialist status (97.6% vs. 64.6%, p<0.001), and a greater pediatric anesthesia volume (43.9% vs. 12.0% with more than 500 cases per year, p<0.001). Regarding the representativity, two items out of four were statistically significant (level of care of institution, annual case load of institution). Regarding the overall anxiety management practices used, there is a heterogeneous response pattern within both groups, with only five out of 17 items showing statistical significance (feasibility of parental presence during induction, known anxiety measurement tools, induction-based prescription of drugs, minimum age and use of non-pharmacological interventions). Conclusions: Although the respondents do not reflect the level of expertise as a survey of a scientific working group, social media surveys on pediatric anesthesia may be feasible to get an overview of a specific topic when there is great heterogeneity overall. In our case, both cohorts showed little difference in the management of preoperative anxiety in daily practice with very heterogeneous approaches. Evidence-based recommendations could help to standardize preoperative anxiety management and improve anxiety levels in children. Clinical Trial: not necessary

  • Individuals receive cancer prevention advice based on ChatGPT and NewBing: specialist evaluation

    Date Submitted: Jul 30, 2024
    Open Peer Review Period: Jul 30, 2024 - Sep 24, 2024

    Background: Cancer prevention holds significant importance in modern healthcare. Artificial intelligence (AI) chatbots have simplified public queries on cancer prevention and increased the effectiveness of prevention strategies. However, further assessment is essential to determine the accuracy and professionalism of the responses provided by these chatbots. Objective: We aimed to investigate the accuracy and consistency of ChatGPT and NewBing in providing cancer prevention advice, and to establish an evaluation framework to support the enhancement of cancer prevention strategies. Methods: We followed guidelines from the American Cancer Society and the National Comprehensive Cancer Network to develop 25 questions after consultations with oncologists. These questions cover four modules on cancer lesions, screening, prevention, and risk. ChatGPT 3.5 and NewBing were utilized for generating responses, with questions reiterated to evaluate model answer stability and reliability. 28 specialists from diverse regions rated the model answers on a 1-5 scale based on clinical experience and guidelines. The scores were statistically analyzed by Excel and SPSS to evaluate their accuracy and consistency with the medical community's consensus. Results: In the four modules, the median total scores for ChatGPT and NewBing were 73 and 71.5 (p > 0.05), respectively. For specific questions such as "What is a nodule?" and “What are the early symptoms of cancer in the body?” in the "cancer lesion-related" module, and certain questions in the "cancer screening" and "cancer risk" modules, ChatGPT scores higher than NewBing (p < 0.05). Subgroup analyses were conducted between clinicians' and non-clinicians' scores (p < 0.05). Conclusions: Both ChatGPT and NewBing exhibited high accuracy and stability when offering cancer prevention advice, showcasing proficiency in handling intricate medical data. Overall, ChatGPT outperformed NewBing, especially on several specific areas. As AI advances, its role in personalized health services will become more significant. It is essential to consistently enhance model performance, guarantee response accuracy, and address ethical concerns.

  • The efficacy of VR in the application of musculoskeletal diseases: An umbrella review

    Date Submitted: Jul 21, 2024
    Open Peer Review Period: Jul 26, 2024 - Sep 20, 2024

    Background: Musculoskeletal disorders are the leading cause of disability in people, and managing them can be challenging. Virtual reality (VR) technology has been recognized as a promising simulation tool in the field of medicine and rehabilitation, and is an important part of the rehabilitation care of patients in the field of orthopedics. The efficacy of VR interventions for musculoskeletal disorders remains to be determined. Objective: To analyze the impact of the virtual reality on musculoskeletal diseases rehabilitation and assess the consistency of evidence from existing systematic reviews and meta-analyses. Methods: The PubMed/Medline, Embase, and Cochrane Library databases were searched for relevant articles published up to April 2024. Literature screening, quality evaluation, and data extraction were performed based on predefined inclusion and exclusion criteria. The Measurement Tool to Assess Systematic Reviews (AMSTAR) 2 was used to evaluate the methodological quality of the included meta-analyses. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was used to rate the evidence level for each outcome as high, moderate, low, or very low. Furthermore, the ratings were classified into four categories based on the evidence classification criteria: I (convincing); II (highly suggestive); III (suggestive); IV (weak); and non-significant. Results: Results from 15 meta-analyses were synthesized. Seven meta-analyses had high, eight had moderate, and the remaining had low AMSTAR 2 ratings. Virtual reality (VR) shows promising results in musculoskeletal rehabilitation, significantly reducing knee pain (MD=-1.38, 95%CI: -2.32, -0.44, P=.004, I²=94%) and enhancing balance. In Fibromyalgia Syndrome, VR effectively decreases pain (SMD=-0.45, 95%CI: -0.70, -0.20, P<.01), fatigue (SMD=-0.58, 95%CI: -1.01, -0.14, P=.01), anxiety (SMD=0.50, 95%CI: -0.908, -0.029, P=.04), and depression (SMD=0.02, 95%CI: -0.76, -0.15, P=.003), also improving life quality. For back pain sufferers, VR lessens pain-related fears (MD=-5.46, 95%CI: -9.40, -1.52, P=.007, I²=90%) and pain itself (MD=-1.43, 95%CI: -1.86, -1.00, P<.01, I²=95%). Post-arthroplasty, it positively impacts knee functionality (MD=8.30, 95%CI: 6.92, 9.67, P<.01, I²=24%) and lowers anxiety (MD=-3.95, 95%CI: -7.76, -0.13, P=.04, I²=0%). Conclusions: Virtual reality has shown potential value in rehabilitating various musculoskeletal conditions. It can reduce pain, improve psychological state, and promote patient motor function recovery.

  • Navigating Vascular Therapy with Endovascular Surgical Robots: Bibliometric Analysis and Topic Modelling

    Date Submitted: Jul 26, 2024
    Open Peer Review Period: Jul 26, 2024 - Sep 20, 2024

    Background: Vascular diseases are a global healthcare burden. Endovascular surgical robots, offering precision and minimally invasive advantages, are a burgeoning treatment modality within this field. Objective: This study aims to provide a comprehensive bibliometric analysis and topic modeling of the literature on endovascular surgical robots for vascular therapy, identifying key trends, research patterns, and future innovation opportunities Methods: This study performed a bibliometric analysis and topic modeling on English-language articles indexed in the Web of Science Core Collection from 1970 to 2024. A search strategy was collaboratively defined by experts in computing and vascular surgery. The Bibliometrix package in R was utilized for bibliometric analysis, and Latent Dirichlet Allocation (LDA) was employed for thematic modeling following extensive data cleansing and preprocessing. Results: The study encompassed 372 articles from 131 sources (2005-2024), showing an 18.46% annual growth rate in endovascular robotic research. The United States and China are the most prolific contributors, with China overtaking the United States in 2023. The cumulative production of these two countries significantly exceeds others, indicating a dominant position in the field. The analysis identified key journals publishing on the topic, with some engineering and interdisciplinary journals leading in both impact and volume of publications. Medical journals, particularly cardiovascular-focused ones, also showed high citation rates. Prominent institutions and authors, such as Shuxiang Guo and affiliated institutions, were identified as key players. Six main topics were identified, including catheter control and positioning, magnetic navigation systems, robotic endovascular navigation, robotic ablation efficacy, force feedback in surgical automation, and robotic-assisted PCI and stenting. These themes reflect both technical advancements and clinical applications, with a focus on improving procedural precision and patient outcomes. Conclusions: This study maps the landscape of endovascular surgical robots in vascular therapy, revealing growth trends and research patterns. It underscores the importance of international collaboration and identifies future research directions, providing a valuable framework for innovation in this dynamic field.

  • Background: Both obesity and underweight are matters of global concern. Weight-related content frequently shared on social media can reflect public recognition and affect users’ behaviors and perceptions. Although X is one of the most popular social media platforms, few studies have revealed the content of weight-related posts or details of dietary behaviors for weight loss shared on X. Objective: This study aimed to describe body weight-related contents frequently reposted on X (Twitter), with a particular focus on dietary behaviors for weight loss in English and Japanese. Methods: We collected English and Japanese X posts related to human body weight having over 100 reposts in July 2023 using an application programming interface tool. Two independent researchers categorized the contents of the posts into seven main categories, and then summarized recommended weight loss strategies. Results: We analyzed 815 English and 1213 Japanese posts. The most popular main category of the contents was “how to change weight” in both languages. The Japanese posts were more likely to mention “how to change weight” (47.1%) and “recipes to change weight” (9.4%) than the English posts (23.9% and 1.2%, respectively), whereas the English posts were more likely to mention “will or experience to change weight” (20.5%), “attitudes toward weight status” (9.6%), and “public health situation” (5.4%) than Japanese posts (12.3%, 2.6%, and 0.9%, respectively). Among 146 English and 541 Japanese posts about “how to change weight”, the predominant strategies were diet (52.1% in English and 31.4% in Japanese) and physical activities (38.4% and 54.5%, respectively). The proportion of posts mentioning both diet and physical activity was smaller in Japanese (11.5%) than in English (21.2%). Among 76 English and 170 Japanese posts about dietary behaviors for weight loss, more than 60% of posts recommended increasing intakes of specific nutrients or food groups in both languages. The most popular dietary component recommended to increase was vegetables in both English (40.8%) and Japanese (28.2%), followed by protein (38.2%) and fruits (35.5%) in English and by grains/potatoes (18.8%) and legumes (18.8%) in Japanese. Japanese posts were less likely to mention reducing energy intake (12.4%), meal timing or eating frequency (21.8%), or reducing intakes of specific nutrients or food groups (26.5%) than the English posts (30.3%, 34.2%, and 40.8%, respectively). The most popular dietary component recommended to decrease was alcohol in English (28.9%) and sweets/confectioneries in Japanese (9.4%). Conclusions: This study characterized user interest in weight management and suggested the potential of X as an information source for weight management. Although weight loss strategies related to diet and physical activity were popular in both English and Japanese, some differences in the details of the strategies were present, indicating that X users are exposed to different information in English and Japanese.

  • Informal Caregivers Connecting Online: Content Analysis of Posts on Discussion Forums

    Date Submitted: Jul 28, 2024
    Open Peer Review Period: Jul 25, 2024 - Sep 19, 2024

    Background: About 53 million adults in the United States offer informal care to family and friends with disease or disability. Such care has an estimated economic value of $600 million. Most informal caregivers are not paid nor trained in caregiving, with many experiencing higher-than-average levels of stress and depression and lower levels of physical health. Some informal caregivers participate in online forums related to their caregiving role. Objective: This study aimed to explore how informal caregivers use easy access, general caregiving online forums, including the types of information they share and seek from others. It also aimed to gain insights into the informal caregiver experience from the content these informal caregivers posted. Methods: The study population consisted of participants who posted on five publicly accessible online forums for informal caregivers between February and April 2024. Researchers extracted the first six responses to the first 20 questions and comments to appear posted by the informal caregivers in each of the five forums, removing any individually identifying information. We used a codebook thematic analysis approach to examine the data with Dedoose. Researchers independently read all posts and coded the data. The author group discussed the codes, reiteratively refined them, and identified themes within the data. Results: The data consisted of 100 initial posts and 600 responses. Over half of the initial posts included specific questions, with the remaining initial posts sharing experiences or reflections. Posts ranged in length from a sentence to more than 500 words. Domains identified included handling interpersonal challenges, navigating complicated systems, gathering tactical coping strategies, managing emotions, and connecting with others in similar situations. Conclusions: Informal caregivers play an essential role in society. Many experience multifaceted challenges related to their caregiving role, and some turn to the internet for community. Accessing online discussion forums is a low-barrier method for informal caregivers to connect with other informal caregivers who may be experiencing similar emotions and challenges. Gaining greater understanding of the ways informal caregivers seek advice and offer support to one another provides insight into the challenges they face. The domains identified on these forums may be helpful as clinicians provide information to care recipients and their informal caregivers along their health journeys.

  • Background: Vital signs monitoring is crucial in clinical practice, predicting mortality and early signs of deterioration. It is key in scoring systems for patient outcomes. Advances in technology now allow convenient community-based monitoring. Objective: We sought to assess the efficacy of remote vital signs monitoring for patients with acute illnesses treated at home. Due to limited data in the hospital at home setting, we also included post-acute phase studies where patients are vulnerable to deterioration. Methods: A literature search was conducted on January 16, 2023, using the following databases: PubMed (MEDLINE), Embase, and Scopus. All abstracts were screened, and the full texts of potentially eligible studies were accessed to determine their eligibility for data extraction. Risk of bias was assessed using Cochrane Risk of Bias Tool (ROB-2) quality assessment tool for randomised controlled trials, Newcastle-Ottawa scale for observational studies and case methods described by Murad et al for case reports. Estimates of readmission and mortality rates from randomised controlled trials (RCTs) and cohort studies were combined using risk ratios (RR). A random-effects model with the Mantel-Haenszel method was used, considering anticipated between-study differences. Results: A total of 28 studies were reviewed. Hospital readmission within 30 to 60 days were reported in 6 studies while 4 studies reported mortality within the initial 30 days. There was no significant reduction of hospital readmission between 30 to 60 days (Relative Risk [RR] 0.81, 95% Confidence interval [Cl] (0.61 – 1.09)). Conversely, there was a significant reduction of mortality rates (RR 0.65, 95% CI 0.42 – 0.99). There was high heterogeneity in the study populations, interventions and outcomes measured. Many studies were small and of poor quality. Majority of studies also focused on patients in the post-acute phase rather than hospital at home. Conclusions: Published data on the effects of remote VSM in acutely ill patients at home remains scarce. There is a need for additional high-quality studies to explore the optimal application of remote VSM in acutely ill patients receiving care at home.

  • Background: In health research, large databases and biobanks gain evermore in importance, especially against the background of the digital transformation of research through AI- and algorithms-based research innovations. Considering the health domain, some would argue that there is a moral obligation to make the personal health data gathered in databases and biobanks available for secondary research use. Yet, it is still unclear what ways to gain consent to use the stored data for secondary research purposes are both effective and respectful of the patient's autonomy. One prominent example under discussion is broad consent. As a form of consent in which specific study objectives are not defined, it is seen by many as an efficient alternative to the established consent of participants. Research on subjects' attitudes towards and their discursive reflections on broad consent is, however, limited. Objective: With our study, we aimed to gain deeper insights into the views and (normative) attitudes towards broad consent by members and representatives of patient organizations. Methods: Semi-structured interviews were conducted with members (N=13) and representatives (N=9) of German patient organizations. Subsequently, we evaluated the material using content analysis. Results: The results initially indicate a general agreement with broad consent. In contrast to the results of some existing studies on broad consent, our analysis reveals limitations in this regard: Positive assessments relate less to broad consent in particular, but rather to overarching regulations on secondary data use that deviate from broad consent. Broad consent is criticized for exactly what it was originally intended to regulate: reduced flow of information, lack of a concrete and communicated research objective, and coverage of (too) long periods of time. The interviewees often formulated specific ideas and wishes about appropriate procedures for consenting to secondary data use, i.e., specific governance structures, thus (implicitly) conceptualizing "informed consent" as the gold standard of consent procedures. Conclusions: The interviewees consider the provision of data for secondary use to be important for the improvement of treatment methods, freedom of research, and ethical considerations such as solidarity. However, these values are linked to conditions in the stakeholders' considerations and thus appear less absolute and universal than conditional and situated. Qualitative, empirical-ethical research can make this inherent complexity of ethical attitudes of stakeholders as negotiation processes tangible and fruitful for medical ethical practice.

  • Quantifying public engagement with medical science, misinformation, and malinformation

    Date Submitted: Jul 23, 2024
    Open Peer Review Period: Jul 23, 2024 - Sep 17, 2024

    Background: Medical journals are critical vanguards of research, and there is increased public interest in and engagement with medico-scientific findings. How findings propagate and are understood, and what harms erroneous claims might cause to public health remain unclear. Objective: To gauge the engagement of the public with medical science and quantify the propagation patterns of medico-scientific articles. Methods: Altmetric analysis of engagement with a decade of approximately 9.8 million articles from five leading medical journals. Comparative analysis with the proliferation of and sentiment of the article with the highest-ever Altmetric score, containing vaccine-negative malinformation in social media users and media outlets worldwide. Results: Potential scientific malinformation was much more likely to be engaged with and amplified by vaccine-negative twitter accounts than neutral ones (p < 0.00001), with negative editorialization alluding to the ostensible prestige of medical journals. Malinformation was invoked frequently invoked by conspiracy theory websites and non-news sources (39.2% of all citations) online to cast doubt on the efficacy of vaccination, who tended to use that information repeatedly. Conclusions: Our findings suggest growing public interest in medical science and presents evidence that medical and scientific journals need be aware of the harms of potential misinformation and malinformation. Clinical Trial: NA

  • Background: Over the last decade, the healthcare technology landscape has expanded significantly, introducing new and innovative solutions to address healthcare needs. The implications of cybersecurity incidents in the healthcare context extend beyond data breaches to potentially harming individuals' health and safety. Risk perception is influenced by various contextual factors, contributing to cybersecurity concerns that technological safeguards alone cannot address. Thus, it is imperative to study risk perceptions, contextual factors, and technological benefits to guide policy development, risk management, education, and implementation strategies. Objective: To investigate the differences in cybersecurity risk perception among various stakeholders in the healthcare sector in Norway and British Columbia (BC), Canada, and identify specific contextual factors that shape these perceptions. We expect to identify differences in risk perceptions for the explored healthcare technologies. Methods: Using a mixed-methods approach comprising surveys and interviews, we sampled healthcare-related wearable technology stakeholders, including healthcare workers, patients (adults and adolescents) and their families, health authorities and hospital staff (biomedical engineers, IT support, research), and device vendors/industry professionals in both Norway and BC. Surveys explored information security scenarios based on the Behavioural Cognitive Internet Security Questionnaire (BCISQ), risk perception, and contextualizing variables. We analyzed both survey datasets to summarize participants’ characteristics and responses to questions related to the BCISQ (behaviour and attitude) and risk perception. Interviews were analyzed thematically using an inductive-deductive approach to explore risk perception and contextual factors. Results: Data from 274 survey respondents were available for analysis: 185 from Norway, including 139 (75%) females, and 89 from BC, including 57 (64%) females. Forty-five respondents (31 in Norway and 14 in BC) participated in interviews. The BCISQ showed minor differences between locations; respondents demonstrated generally low-risk behaviour and robust information security awareness. However, password simulation demonstrated discrepancies between self-assessed and “real” behaviour by sharing or willingness to share passwords. Perceived risk is generally considered low, yet consequences of cybersecurity risks were evaluated as major but unlikely. Risk perception was stronger for assisted living and diabetes technologies than for smartwatches. The most important contextual factors shaping risk perceptions are human factors encompassing knowledge, competence, familiarity, feelings of dread, perceived benefit, and trust, as well as the technological factor of device functionality. Organizational and technological factors had lesser effects. Conclusions: We found minimal differences in behaviour and risk perception among Norwegian and BC participants. Human factors and device functionality were most influential in shaping cybersecurity risk perceptions. Considering the rising need for assisted living technologies and wearables, insight into risk perceptions can strengthen risk management, awareness, and competence building. Further, it can address potential concerns amongst stakeholders to enable quicker technology adoption.

  • Background: The COVID-19 public health emergency (PHE) catalyzed widespread adoption of telemedicine, for both video and audio-only visits. This proliferation highlighted inequities in healthcare access by age, race, ethnicity, and preferred language. Few studies have investigated how differences in health system telemedicine implementation affected these inequities. Objective: To describe the characteristics of patients who utilized telemedicine during the PHE and identify predictors of telemedicine use across health systems with different telemedicine implementation. Methods: This retrospective cohort study included adults with diabetes receiving primary care 7/2020-3/2021 at two independent health systems in San Francisco. Participant sociodemographic characteristics, health information, and telemedicine utilization were derived from electronic health records. The primary outcome was visit type (any audio or video telemedicine vs. in-person only) during the study period. We used multivariable logistic regression to assess the association across health systems between visit type and key predictors associated with digital exclusion (age, race/ethnicity, preferred language, and neighborhood socioeconomic status), adjusting for baseline health information. We included an interaction term to estimate health system impact on each predictor, then stratified by health system (academic, which prioritized video-enabled visits, vs. safety-net, which prioritized audio-only visits). Results: Among 10,201 patients, results from our multivariable analyses demonstrated higher odds of telemedicine use in the safety-net system compared to the academic system (aOR 2.94, 95%CI 2.48-3.48). Overall, younger age (age 18-34: aOR 2.55, 95%CI 1.63-3.97; age 35-49: aOR 1.39, 95%CI 1.12-1.73 vs. age 75+) and Chinese language preference (aOR 2.04, 95%CI 1.66-2.5 vs. English) had higher odds of having a telemedicine visit. Conversely, patients had lower odds of having a telemedicine visit if they were non-Hispanic (NH) Asian (aOR 0.67, 95%CI 0.56-0.79), NH Black (aOR 0.83, 95%CI 0.68-1), Hispanic/Latine (aOR 0.76, 95%CI 0.61-0.95) compared to NH White patients. The model with the interaction term demonstrated significant interactions between health system and age, race and ethnicity, and preferred language. After stratifying by health system, several differences persisted in the academic health system: NH Asian patients and Hispanic/Latine patients had lower odds of a telemedicine visit (Asian aOR 0.57, 95%CI 0.46-0.70, Latine aOR 0.67, 95%CI 0.50-0.91) and younger age groups had higher odds (ages 18-34: aOR 3.97, 95%CI 1.99-7.93, ages 35-49: aOR 1.86, 95%CI 1.36-2.56). In the safety-net system, Chinese-speaking patients had a higher likelihood of having a telemedicine visit (aOR 2.52, 95% CI 1.85, 3.42). Conclusions: The study demonstrates disparities in telemedicine utilization by age, race and ethnicity, and language, primarily in the health system that utilized more video visits. While telemedicine has expanded rapidly during the PHE, certain populations remain at risk for digital exclusion. These findings suggest that system-level factors may significantly influence telemedicine adoption. Implementing both audio-only and video options may enhance accessibility for populations at risk for digital exclusion.

  • Factors Influencing Implementation and Adoption of Digital Nursing Technologies – Systematic Umbrella Review

    Date Submitted: Aug 12, 2024
    Open Peer Review Period: Jul 22, 2024 - Sep 16, 2024

    Background: Digital nursing technologies (DNTs) are a promising solution to address challenges in healthcare systems, such as demographic shifts and nursing shortages. Despite their potential benefits, the integration of DNTs into care settings remains complex due to multiple factors influencing implementation and adoption. Objective: This review examines factors that influence the implementation and adoption of DNTs used in formal care settings, as seen by nurses. Objective: This review examines factors that influence the implementation and adoption of DNTs used in formal care settings, as seen by nurses. Methods: We used an umbrella review methodology to synthesize the evidence on DNTs and the complexities of their implementation. A systematic search of PubMed, CINAHL, Cochrane Library, and Business Source Premier from March to April 2023 identified reviews that focused on DNTs in formal care settings. Two researchers independently performed data extraction and quality assessment. Data analysis was structured by embedding the results in the NASSS framework, a model for explaining the adoption, abandonment and challenges to the scaling, diffusion and sustainability of health and care technologies. Results: A total of 3775 reviews were identified. 51 met the inclusion criteria and were included in this review. We identified influencing factors in six NASSS domains. Technology-related barriers included usability issues and technical challenges, impacting workflow efficiency and care quality. Perceived benefits included enhanced care quality and efficiency, although challenges in training and education were noted among adopters. Organizational factors such as a supportive environment and IT infrastructure were critical, while broader systemic issues included funding constraints and unclear reimbursement models. Conclusions: This review provides insights into DNTs' implementation in formal nursing care, highlighting critical factors influencing adoption and implementation success. Addressing these factors is crucial for maximizing DNTs benefits in healthcare settings, enhancing nursing practice, and ultimately improving patient outcomes.

  • Tele-nursing perceptions, needs, and related influences in T2DM patients: a qualitative study

    Date Submitted: Jul 21, 2024
    Open Peer Review Period: Jul 20, 2024 - Sep 14, 2024

    Background: Type 2 Diabetes Mellitus (T2DM) is a growing global health concern, with increasing prevalence necessitating innovative management strategies. Tele-nursing, utilizing digital health technologies, offers a promising solution to enhance the management and care of T2DM patients. However, there is limited understanding of T2DM patients' awareness and acceptance of tele-nursing services, as well as their specific needs and the factors influencing their utilization of these services. Objective: To understand the perceptions and needs of T2DM patients regarding tele-nursing and to analyze the influencing factors, providing a scientific basis for the development and implementation of tele-nursing. Methods: A descriptive qualitative research method was employed. From June to August 2023, a purposive sampling method was used to select 20 T2DM patients from a tertiary hospital, following the principle of maximum variation. Semi-structured interviews were conducted, and the data were analyzed using thematic analysis. Results: Four main themes were identified: insufficient awareness and willingness to use tele-nursing, needs for tele-nursing services, facilitating factors for tele-nursing services, and barriers to tele-nursing services. Conclusions: The awareness of tele-nursing among T2DM patients needs to be further enhanced. Currently, the needs for tele-nursing are relatively singular, focusing mainly on health education and medication reminders, influenced by the accessibility of services, service items, and related costs. Tele-nursing should be guided by the specialized nursing needs of diabetes, combining remote technology with multidisciplinary teams to achieve diversified and remote diabetes care, gradually meeting the multi-level needs of patients.

  • Preventing adolescents' problematic social media use: Parents be on time!

    Date Submitted: Jul 12, 2024
    Open Peer Review Period: Jul 12, 2024 - Sep 6, 2024

    Background: Concerned about adolescents' problematic social media use, many parents apply restrictive mediation. However, its effectiveness remains unclear. Objective: Therefore, this study aimed to provide insights into the specific groups and conditions under which restrictive mediation may effectively prevent adolescents' problematic social media use. Specifically, we investigated the prospective relationship between rules about amount, location and moment of Internet use and the onset of adolescents’ at-risk/problematic social media use. Additionally, we examined the moderating role of demographic and parenting factors, including adolescents’ age, adolescents’ gender, adolescent involvement in rule-setting, positive parenting, parental phubbing, and quality of co-parenting (two-way interactions). Furthermore, we explored whether the moderation effects of the parenting factors varied by adolescents’ age and gender (three-way interactions). Methods: Four wave survey data of 315 adolescents (T1: M age = 13.44 years, SD = 2.26, 46.3% girls) and their parents (T1: M age = 46.4 years, SD = 5.05, 55.4% mothers) were used. Results: Analyses revealed that setting internet-specific rules may prevent the development of problematic social media use symptoms in adolescents aged < 12.31 years, but may be counterproductive for adolescents aged > 15.70 years. No other significant two- and three-way interaction effects were found. Conclusions: These findings highlight the importance of age-appropriate parental mediation strategies to prevent problematic social media use.

  • Safety and efficacy of a modular digital psychotherapy for social anxiety: A randomized controlled trial

    Date Submitted: Jul 9, 2024
    Open Peer Review Period: Jul 9, 2024 - Sep 3, 2024

    Background: Social anxiety disorder is a common mental health condition characterized by an intense fear of social situations which can lead to significant impairment in daily life. Cognitive behavioral therapy (CBT) has been recognized as an effective treatment; however, access to therapists is limited and the fear of interacting with therapists can delay treatment seeking. Furthermore, not all individuals respond. Tailoring modular treatments to individual cognitive profiles may improve efficacy. We developed a novel digital adaptation of CBT for social anxiety that is both modular and fully digital without therapist in the loop and implemented it in a smartphone app. Objective: To evaluate the safety, acceptability and efficacy of the new treatment in online participants with symptoms of social anxiety Methods: Two online randomized controlled trials comparing individuals with access to the treatment through the app to waitlist. Participants were recruited online and reported Social Phobia Inventory (SPIN) total scores >= 30. Primary outcomes were safety and efficacy over 6 weeks in 102 women aged 18-35 (RCT #1) and symptom reduction (Social Phobia Inventory total scores) after 8 weeks in 267 men and women aged 18-75 (RCT #2). Results: In RCT #1, active and control arm adverse event frequency and severity was not distinguishable. App acceptability was high. Secondary outcomes suggested greater symptom reduction in the active (-9.83 ± 12.80) than the control arm (-4.13 ± 11.59, t90 = -2.23, pFDR = .037, Cohen's d = 0.47). In RCT #2, there was a higher symptom reduction in the active arm (-12.89 ± 13.87) than the control arm (-7.48 ± 12.24, t227 = -3.13, pFDR = .008, Cohen's d = 0.42). Conclusions: The online-only, modular social anxiety CBT programme appears safe, acceptable and efficacious in an online patient group with self-reported symptoms of social anxiety. Clinical Trial: RCT #1: ClinicalTrials.gov NCT05858294, RCT #2: ClinicalTrials.gov NCT05987969

  • Evaluation of a digital reproductive health program in Rwanda implemented in Youth Centers: Results from a mixed-methods serial cross-sectional study

    Date Submitted: Jul 30, 2024
    Open Peer Review Period: Jul 9, 2024 - Sep 3, 2024

    Background: Age-appropriate interventions are needed to provide accurate, comprehensive sexual and reproductive health (SRH) information and expand access to services as unmet contraceptive need among adolescents remains a significant public health issue. Digital interventions can potentially address this need, although inequities in digital access remain a barrier. The CyberRwanda digital learning program aims to improve the health and livelihoods of Rwandan adolescents by providing digital access to SRH information and direct links to local youth-friendly services. Objective: Objective: We sought to explore the effectiveness of CyberRwanda within youth centers on behavioral outcomes relevant to contraceptive method uptake and use, as well as characterize its implementation. Methods: Using a serial cross-sectional design, we randomly selected youth center attendees for assessment at baseline and one year following CyberRwanda implementation across nine youth centers within Rwanda. Sampling targets were set specifically for distinct age and gender groups, including younger (12- 14 years) and older (15-19 years) boys and girls. We compared outcomes relevant to contraceptive use from baseline to endline for each age/gender group and assessed engagement patterns with CyberRwanda. A post-hoc propensity score analysis was conducted to estimate the average treatment effect comparing exposed and unexposed groups with respect to contraceptive-related outcomes. We also conducted qualitative in-depth interviews with a subset of CyberRwanda users, implementers, and key stakeholders to expound on quantitative findings. Results: Overall, 2530 young people participated in the study, including 456 young boys, 441 younger girls, 828 older boys, and 805 older girls, with approximately equal numbers of participants recruited at baseline (n=1262) and endline (n=1268). Approximately 50% of study participants reported being aware of CyberRwanda at endline. CyberRwanda engagement (defined as reported use of CyberRwanda) was relatively low among the study population, with 12% reporting use at endline. Comparing baseline to endline, intent to use contraception, self-efficacy related to contraceptive use negotiation, and ability to access contraception was high across all groups and timepoints, with no differences identified. Overall knowledge about fertility was low, although most age/gender groups demonstrated improvements over time. At both timepoints, a majority of sexually active older boys and girls reported use of a modern contraceptive method, mostly male condoms (88.6% and 89.8% at endline, respectively). Results from the propensity score analysis suggest that exposure to CyberRwanda was associated with higher knowledge of modern FP methods and fertility awareness. Qualitative results suggest that CyberRwanda users were satisfied with the program and found it useful; however, stakeholders noted several implementation challenges. Conclusions: Conclusions: Overall the study found some encouraging trends in SRH outcomes, including knowledge; however, it also demonstrated low CyberRwanda engagement within youth centers, thus limiting our ability to fully attribute results to the intervention. Results suggest that CyberRwanda implementation within Youth Centers demonstrates potential value as well as challenges.

  • Colorectal Cancer Racial Equity Post Volume, Content, and Exposure: Observational Study Using Twitter Data

    Date Submitted: Jul 8, 2024
    Open Peer Review Period: Jul 8, 2024 - Sep 2, 2024

    Background: Racial inequity in health outcomes, especially in colorectal cancer (CRC), is among the most pressing issues in cancer communication. However, few studies have focused on the availability and potential reach of racial health equity content on social media. Objective: To examine the volume and content of, as well as exposure to, CRC racial health equity tweets from CRC equity disseminator accounts on Twitter (or X), defined as accounts that disseminate information related to racial equity concerning CRC outcomes. Methods: We identified Twitter accounts that posted CRC tweets between 2019 and 2021 and followed at least two CRC racial equity organization accounts as CRC equity disseminators. We analyzed the volume and content of racial equity related CRC tweets from these equity-disseminator accounts overall and by account types (experts vs. non-experts) and ascertained which types of accounts did the best job of exposing their followers to CRC racial equity content. Results: Only 5.8% of unique tweets from 798 CRC equity disseminators mentioned racially and ethnically minoritized groups. Of these tweets, most (57%) noted outcome disparities, but specific information about CRC symptoms (1.0%) and screening procedures (14.0%) were rare. Of the equity-information disseminators, expert accounts were more likely than non-experts to send CRC equity tweets. Broker accounts, or those with a significant portion of their followers subscribing to them as unique sources of equity content, were those that disseminated equity information most widely to community that would otherwise not learn about this topic. Conclusions: The analysis highlighted the disparate roles of expert and broker accounts in diffusing information with important implications for addressing cancer in racially minoritized groups in CRC. Public health practitioners should encourage CRC equity disseminators on social media to describe symptoms and screening procedures/benefits, and increase the reach of such content on social media.

  • Background: Affecting millions of people, spinal pain is the leading cause of years lived with disability worldwide since the nineties. Centralization phenomenon (CP) and directional preference (DP) are common features in spinal pain patients, indicating a good clinical prognosis. CP is defined as a rapid and lasting migration of distal pain from the limb to the center of the spine after repeated movement tests. DP is a broader concept in which both the migration and decrease of pain are considered. While their detection has still been based on clinician decision, digital tool would provide objective measurements. Objective: We developed and assessed the reliability and validity of a new algorithm to detect CP and DP among spinal pain patients using quantitative a pain mapping software (PRISMAP). Methods: We designed a two-phase retrospective, cross-sectional, double-blinded diagnostic accuracy study. In Phase 1, using PRISMAP, we recorded and analyzed pain variations before and after a CP-focused physiotherapy session with specialized physiotherapy (PT). PTs classified patients as CP+ or CP-. We developed an algorithm to model changes in pain topography and intensity, identifying patients relative to PT classification. In Phase 2, PTs conducted four pain mappings. The initial two maps depicted a three-day overall patient pain profile, from which reliability and agreement were calculated using Intraclass Coefficient Correlation (ICC), Standard Error of Measurement (SEM), Coefficient of Variation (CV), and Bland-Altman analysis (BA). PTs then documented t-time pain mapping pre- and post-repeated movement test, classifying patients into CP-/CP+ and DP-/DP+. Validity parameters (sensitivity, specificity, positive and negative likelihood ratios (LR+/LR-)) were calculated from the latter two maps, using PT classification as the standard reference. Results: Twelve patients were included in Phase 1 and 49 in Phase 2. The algorithm demonstrated good reliability (ICC=0.993 [95%CI 0.988–0.996], SEM=0.211, CV=12.2%, and bias error with the BA of -0.041 representing 2.4% of the sample mean). Validity for CP was 92.0% [95%CI 73.7–99.02], 79.2% [95%CI 57.8–92.9], 4.42 [95%CI 2.01–9.71], 0.1 [95%CI 0.03–0.39] for sensitivity, specificity, LR+, and LR-. Validity for DP was 81.3% [95%CI 63.56–92.79], 88.2% [95%CI 63.6–98.5], 6.91 [1.86–25.66], and 0.21 [95%CI 0.1–0.45] for sensitivity, specificity, LR+, and LR-. Conclusions: The mathematical modeling of CP and DP is reliable and valid. This approach may enhance patient selection for future studies and serve as a clinical aid for practitioners.

  • Background: The integration of the Internet of Things (IoT) into healthcare is revolutionizing the industry by enhancing acute disease care, managing chronic diseases, and supporting self-health management. The COVID-19 pandemic has accelerated the adoption of IoT devices, particularly wearable medical devices (WMDs), which offer real-time health monitoring and advanced remote health management. Globally, the integration and increased adoption of IoT in healthcare has led to enhanced efficiency, improved patient care, and generated significant economic value. Objective: This review aims to conduct a comprehensive meta and weight-analysis synthesizing findings from primarily quantitative articles to identify the most influential predictors and theories explaining the adoption process of IoT in healthcare Methods: A keyword search across electronic databases led us to the analysis of 68 papers with 72 datasets. We conducted a weight analysis, to identify the relationships with the most significant results. We also have conducted a meta-analysis by calculating the average beta values and their significance. Finally, we combined the results from both methods to visualize the most used theories. Results: A significant portion of studies are conducted in China, South Korea, and the United States. The technology acceptance model (TAM) and unified theory of use and acceptance of technology (UTAUT) were the most extensively used theories. The results highlight the importance of fostering positive perceptions toward IoT healthcare by mitigating perceived risks, emphasizing ease of use, and performance benefits. Leveraging performance impacts, the fun, and enjoyment derived from these technologies, the positive perceptions of family and doctors, and resource and support availability is going to promote intention to use. Promoting IoT healthcare technologies to innovative individuals and those motivated by health is more effective. Conclusions: Behavioral intention is the most studied variable, while attitude, performance, effort expectancy, and task-technology fit are less explored, indicating a gap in understanding their predictors. Adoption theories from the information systems field are predominantly used, but integrating health-specific theories can provide deeper insights into individual health motivations and threat perceptions. Future research should focus on understudied variables with conflicting results, predictors with fewer studies, and incorporate qualitative methods to gain deeper insights into the adoption process.