Category: 3. Business

Introduction

Artificial intelligence (AI) in health care broadly refers to the use of advanced computational techniques and algorithms, including machine learning, deep learning, natural language processing, large language and image models, and computer vision to extract insights from complex medical data and enhance clinical decision-making [,]. By augmenting human expertise with data-driven insights, AI has the potential to revolutionize multiple aspects of health care delivery, from early disease detection [,] and drug discovery [,] to improving operational efficiency and resource allocation in health care systems []. Advanced generative AI models can help analyze a variety of digital content, including clinical images, videos, text, and audio, as well as clinical data from electronic health records []. The global AI in health care market is projected to grow at a compound annual growth rate of 36.83% from 2024 to 2034, increasing from US $26.69 billion to US $613.81 billion [], signaling a significant shift in how health care is delivered and managed. This rapid integration of AI into clinical practice has generated both excitement and concern among health care professionals, policymakers, and patients. As AI becomes more prevalent in clinical settings, there is a growing concern about its unintended consequences. AI could deepen the digital divide and increase disparities, especially among older adults, low-income groups, and rural communities []. Further, the ethical and regulatory challenges surrounding the use of AI in health care continue to be widely debated [].

While the transformative potential of AI in health care is well understood, its expanded adoption raises important questions about its impact on patient care, equity, and ethics. Patients, as the ultimate beneficiaries of AI-driven health care solutions, play a critical role in shaping how AI technologies are designed, implemented, and trusted. However, patient perspectives on AI remain underexplored. Understanding these perspectives is important for many reasons. First, it ensures patient trust and acceptance, which are essential for successful implementation of AI-based health care solutions []. Second, it helps address ethical concerns and potential biases in AI algorithms, which could lead to unequal access to care or diagnostic errors []. Third, a good understanding of patients’ views on AI allows for tailoring AI solutions that align with patient needs and preferences, ultimately improving health outcomes and satisfaction [].

Despite the importance of understanding patient perspectives on AI use in clinical practice, research in this area remains limited. While several studies have explored health care professionals’ attitudes toward AI [-], comparatively little attention has been given to patients’ attitudes and concerns. Some studies have reported a generally positive patient attitude toward the use of AI in health care [,], though they also highlight concerns about privacy and control over personal health data. Other studies have documented patient resistance [], distrust in AI [], and a preference for restricting AI use to nonclinical tasks such as administrative or scheduling functions []. In addition, studies have also identified patient apprehensions due to perceived safety risks, threats to patient autonomy, potential increases in health care costs, algorithmic biases, and data security issues [].

This study examines patients’ knowledge levels regarding AI in health care, their comfort with AI use across clinical applications, and their attitudes toward the use of personal health information for AI purposes. Our research framework is shown in . We also explore how sociodemographic characteristics, digital health literacy, and health conditions are associated with patient attitudes and comfort levels. Specifically, we address two research questions: (1) What are the levels of public knowledge and comfort with AI in health care, including the use of personal health data with and without consent? and (2) How do sociodemographic factors, digital health literacy, and chronic health conditions influence these attitudes? By addressing these questions, this study aims to fill critical gaps in understanding patient perspectives and to inform ethical, equitable, and effective implementation of AI solutions in health care.

Methods

Dataset

Data used in this study were obtained from the 2023 Canadian Digital Health Survey (CDHS) that was commissioned by Canada Health Infoway to assess Canadians’ experiences and perceptions regarding digital health services, including the use of AI in health care [,]. The web-based survey was administered by Leger, one of Canada’s leading market research firms, between November 28 and December 28, 2023. Participants, aged 16 years and older, were recruited from Leger Opinion’s nationally representative online panel using computer-assisted web interviewing technology. A total of 10,130 respondents participated in the survey, which was available in both English and French.

Ethical Considerations

The 2023 Canadian Digital Health Survey obtained informed consent from its 10,130 participants and adhered to the public opinion research standards of the Canadian Research and Insights Council and the global ESOMAR (European Society for Opinion and Marketing Research) network to ensure methodological rigor and data quality [,]. Information about respondents was deidentified and anonymized to protect privacy and confidentiality. Patients provided consent for data collection and evaluation.

Variables

The survey assessed four key variables related to patients’ perceptions of AI in health care using 4-point ordinal scales. To evaluate participants’ understanding of AI, they were asked to rate their knowledge on a scale from 1 (not at all knowledgeable) to 4 (very knowledgeable). The question “How comfortable are you with AI being used as a tool in health care?” was used to assess participants’ comfort level on a scale ranging from 1 (very uncomfortable) to 4 (very comfortable). A similar approach was used to assess attitudes toward the use of personal health data in AI research. Participants were asked how comfortable they felt about scientists using their personal health data for AI research when informed consent was provided, using the same 4-point scale. To examine privacy concerns, the survey also asked about comfort levels regarding AI research using deidentified health data without explicit consent. Participants were also asked about their comfort levels in applying AI in 7 areas: monitoring and predicting health conditions, decision support for health care professionals, precision medicine, drug and vaccine development, disease monitoring at home, tracking epidemics, and optimizing health care workflows.

Participants were asked to self-report any serious or chronic health conditions diagnosed by a health professional. The survey defined chronic illness as a condition expected to last, or already lasting, 6 months or more. Respondents could choose from a predefined list of 15 chronic conditions: chronic pain, cancer, diabetes, cardiovascular disease, Alzheimer’s disease, developmental disabilities, obesity, mental health conditions, and physical or sensory disabilities. Additionally, participants had the option to specify any other chronic illness not listed or indicate no chronic illness. We calculated a composite score representing the total number of chronic conditions reported by each respondent.

Digital health literacy was assessed using 8 items from eHealth Literacy Scale [], which measures the ability to find, evaluate, and use health information on the internet. Items were rated on a 5-point Likert scale (1=strongly disagree to 5=strongly agree). After confirming their convergence and reliability using exploratory principal component analysis with varimax rotation (which extracted one factor, confirming unidimensionality) and Cronbach α (0.934), responses were summed to create a digital health literacy score, reflecting a respondent’s overall proficiency in navigating and utilizing online health resources. The following sociodemographic variables were also captured in the survey: age, sex, annual household income, citizenship, race, educational attainment, and employment status.

Analytic Sample and Nonresponse Bias

CDHS collected data from 10,130 Canadian adults on a broad range of topics related to digital health. Given this study’s focus on AI use in health care, only 6904 respondents who provided complete responses to AI-related questions were included in the analytic sample.

To assess potential selection or nonresponse bias, χ² tests were conducted to compare included and excluded respondents across all the sociodemographic variables: age, sex, income, education, race, employment, and citizenship. The χ² tests revealed significant differences between the overall respondents and our analytic sample across five variables (age, sex, employment, education, and income; P<.05), with our analytic sample overrepresenting males (53.2% vs 48.6%), individuals aged 25‐54 years (51.8% vs 48.6%), higher household incomes (35.7% above CAD 100,000 vs 33.6%), higher education levels (eg, graduate college or above: 39.7% vs 37.4%), and employed respondents (61.6% vs 58.6%).

To address this bias, we derived inverse probability weights (IPW) to adjust for the likelihood of inclusion in the analytic sample. The IPW values were estimated using a logistic regression model predicting inclusion based on sociodemographic variables. These weights were then combined with the original CDHS survey design weights (which account for sampling and nonresponse at the national level) to create a composite total weight. This combined weighting approach ensured that both survey design and sample selection bias were accounted for in all weighted analyses.

Statistical Analysis

All statistical analyses were conducted using STATA 18 software. Descriptive statistics summarized respondent characteristics. To evaluate potential selection bias, the IPW was derived as described above.

Ordinal logistic regression models were estimated for four AI-related attitudinal outcomes: (1) knowledge of AI in health care, (2) comfort with use of AI in health care, (3) comfort with use of personal health data for AI with consent, and (4) comfort with use of personal health data for AI without consent. Each model was estimated three ways: unweighted, nonresponse-adjusted weighted (IPW), and fully weighted (combining IPW and CDHS-provided survey design weights) to evaluate robustness. All weighted models were estimated using survey (svy) commands in STATA to account for the complex survey design. Potential multicollinearity among predictors was assessed using Cramer’s V for categorical variables and variance inflation factors from proxy linear regressions.

Results

The demographic profile of survey respondents is presented in . Our dataset had the majority of respondents aged 35‐54 years (2412, 34.94%), followed by those aged 65+ years (1585, 22.96%) and 55‐64 years (1211, 17.54%). There was a slight majority of male respondents (53.2%, 3673), with female respondents comprising 46.8% (3231) of the sample. Regarding household income, 4699 (68.06%) reported earnings of CAD 60,000 or more, while 2205 (31.94%) earned less. The majority were Canadian citizens (6581, 95.32%), with noncitizens (323, 4.68%) forming a smaller proportion. Racially, the sample had predominantly White respondents (5104, 73.93%), followed by Asian-origin (972, 14.08%), other (575, 8.33%), and Black or African-origin (253, 3.66%) respondents. Education levels varied, with most having at least some college education (2831, 41.01%) or a graduate degree (2738, 39.66%). Fewer had a high school diploma (1174, 17%) or less than high school education (161, 2.33%). Employment status showed that 4253 (61.6%) were employed.

Our analysis found varying levels of knowledge levels and comfort regarding AI use in health care among respondents (). While a majority (2919, 42.3%) reported being moderately knowledgeable about AI, only 7.8% (542) considered themselves very knowledgeable. Conversely, nearly half of the respondents (49.9%) considered themselves less knowledgeable, with 38.7% (2669) reporting they were “not very knowledgeable” and 11.2% (774) reporting “not at all knowledgeable.”

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When it comes to AI use in health care, 44.6% (3077) of respondents reported being moderately comfortable, while 42.4% (2927) expressed some level of discomfort. Comfort levels increased when AI involved use of personal health data under informed consent, with 64.7% (4466, moderately or very comfortable) supporting such AI use. However, comfort levels declined when AI research used deidentified data without consent, with only 47.4% (3272) reporting comfort and 52.6% (3632) expressing discomfort. When asked about comfort levels pertaining to AI use in various health care areas (), moderate comfort levels (40%‐47%) were observed across all areas. However, respondents expressed relatively greater support for AI use in tracking epidemics and optimizing health care workflows, where a higher proportion of respondents felt “very comfortable” compared to other areas.

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presents results from the fully weighted ordinal logistic regression models assessing associations between respondents’ self-reported levels of knowledge about AI and their sociodemographic characteristics, digital health literacy, and health conditions. These results were consistent with those from the unweighted and nonresponse-adjusted models, showing similar effect sizes and significance patterns ().

Age was a significant predictor, with respondents in older age groups exhibiting lower odds of having higher AI knowledge compared to those aged 16‐24 years: 25‐34 years (odds ratio [OR] 0.69, 95% CI 0.54‐0.87; P<.001), 35‐54 years (OR 0.59, 95% CI 0.47‐0.73; P<.001), 55‐64 years (OR 0.44, 95% CI 0.35‐0.56; P<.001), and 65+ years (OR 0.39, 95% CI 0.31‐0.49; P<.001). Men were significantly more likely to report higher AI knowledge than women (OR 1.57, 95% CI 1.42‐1.73; P<.001).

Among socioeconomic factors, those with higher annual household incomes (CAD >100,000) exhibited higher odds for greater AI knowledge (OR 1.29, 95% CI 1.14‐1.45; P<.001), while those with lower incomes (CAD <$60,000) showed no significant difference (OR 1.07, 95% CI 0.94‐1.22; P=.34). Noncitizens exhibited higher AI knowledge levels (OR 1.71, 95% CI 1.32‐2.21; P<.001) compared to citizens. Race also was a significant factor, with White respondents exhibiting lower odds of AI knowledge (OR 0.79, 95% CI 0.68‐0.93; P<.001) relative to Asian-origin respondents, while differences for Black or African-origin and Other groups were not statistically significant.

Education was another key predictor, with graduates showing significantly higher AI knowledge (OR 1.43, 95% CI 1.27‐1.60; P<.001) compared to those with a college-level education, while respondents with only a high school education or less showed no significant difference. Employment status was not significantly associated with the odds of reporting higher AI knowledge.

Higher digital health literacy was strongly associated with increased AI knowledge (OR 1.08, 95% CI 1.07‐1.09; P<.001). Additionally, respondents with more chronic health conditions had higher odds of reporting greater AI knowledge (OR 1.08, 95% CI 1.03‐1.12; P<.001), suggesting that health experiences may influence awareness of AI applications.

presents results from the fully weighted ordinal logistic regression models, each examining the association between respondents’ comfort levels with AI in health care, the use of personal health data for AI with and without consent, and key factors including sociodemographics, digital health literacy, and health conditions. Nonresponse weighted and unweighted models (Tables 4–6 in ) yielded results similar to the fully weighted analyses, supporting the sensitivity and robustness of the findings.

Age showed significant association with comfort levels with AI use in health care. In Model 1, older adults aged 65+ years exhibited higher odds of greater comfort with AI in health care (OR 1.47, 95% CI 1.17‐1.84; P<.001) compared to respondents aged 16‐24 years. In Model 2, respondents aged 35‐54 years (OR 0.72, 95% CI 0.59‐0.89; P<.001) exhibited lower odds of comfort when personal health data were used in AI with consent, while other age groups showed no statistically significant difference. In Model 3, comfort declined further among respondents aged 35‐54 years (OR 0.73, 95% CI 0.60‐0.88; P<.001) and 55‐64 years (OR 0.77, 95% CI 0.63‐0.95; P=.01) when personal health data were used in AI without consent, suggesting greater sensitivity to consent among middle-aged adults.

Sex was a consistent predictor across all three models, with men exhibiting higher odds of greater comfort of AI use in health care than women (Model 1: OR 1.50, 95% CI 1.36‐1.65; P<.001; Model 2: OR 1.39, 95% CI 1.27‐1.53; P<.001; Model 3: OR 1.56, 95% CI 1.42‐1.71; P<.001), indicating that men consistently report greater comfort with AI use in health when personal health data were used irrespective of consent.

Respondents with higher annual household incomes (above CAD 100,000) were significantly more likely to be comfortable with AI use in health care (OR 1.21, 95% CI 1.08‐1.37; P<.001) and with the use of personal data with consent (OR 1.16, 95% CI 1.03‐1.30; P=.01) when compared to those earning between CAD 60,000 and 100,000. Further, lower-income respondents (CAD <60,000) consistently reported lower comfort levels across all three models (OR range 0.83‐0.87, P<.05), suggesting that financial disparities may influence attitudes toward AI in health care.

We also found citizenship status in Canada to be a significant predictor in 2 out of 3 models. Noncitizens exhibited higher odds of comfort with AI use in health care (OR 1.49, 95% CI 1.18‐1.89; P<.001) and when personal health data were used without consent (OR 1.28, 95% CI 1.02‐1.61; P=.03), though the association was not significant in the with-consent model (OR 1.20, 95% CI 0.96‐1.49; P=.11). Overall, these findings suggest that noncitizens may perceive AI applications in health care more positively than citizens and are likely to exhibit more comfort level in personal health data being used for AI applications in healthcare irrespective of the consent.

Compared with Asian-origin respondents, White respondents exhibited lower odds of comfort across all models (OR range=0.69‐0.78, P<.001). Those identifying as “Other” racial groups also had lower odds in Models 1 and 2 (OR range=0.77‐0.78, P<.05). For Black or African-origin respondents, the association was significant only in Model 3 (OR 0.71, 95% CI 0.54‐0.94; P=.02), indicating reduced comfort when personal health data were used without consent.

Our analysis also showed higher educational attainment to be positively associated with comfort with AI use in health care and when personal health data were used with consent. Respondents with graduate-level or more education were significantly more comfortable (Model 1: OR 1.29, 95% CI 1.15‐1.44; P<.001; Model 2: OR 1.25, 95% CI 1.12‐1.40; P<.001). Those with only a high school education or lower did not show significant differences compared to the reference group (college-level education).

Digital health literacy emerged as a strong and consistent predictor across all three models. Each one-unit increase in digital literacy was associated with a 4%‐6% increase in odds of greater comfort (Model 1: OR 1.06, 95% CI 1.05‐1.07; P<.001; Model 2: OR 1.05, 95% CI 1.04‐1.06; P<.001; Model 3: OR 1.04, 95% CI 1.03‐1.05; P<.001), indicating that individuals with greater proficiency in using digital health tools were more comfortable with AI use in health care, both in general health care settings and when personal health data were involved.

The number of chronic health conditions was positively associated with comfort in Models 1 (OR 1.04, 95% CI 1.00‐1.08; P=.04) and 2 (OR 1.07, 95% CI 1.03‐1.11; P<.001) but not in Model 3 (OR 1.03, 95% CI 0.99‐1.07; P=.12), suggesting that individuals with multiple chronic illnesses were more comfortable with AI use in health care, particularly when personal data were used with consent. Employment status was not significantly associated with AI comfort in any of the three models.

Discussion

Principal Findings

To our knowledge, this is one of the first few studies to examine public attitudes toward use of AI in health care, with specific focus on the influence of sociodemographic, digital health literacy, and health-related factors. Overall, study respondents reported mixed levels of knowledge about AI and a considerable proportion (42.39%) expressing discomfort with AI use in health care. When personal health data were used for AI solutions with consent, the proportion of individuals becoming comfortable with AI use increased (64.69%), and when AI applications used personal deidentified health data without consent, a higher proportion (52.61%) expressed discomfort. A relatively higher proportion of respondents expressed greater comfort when AI was used in nonclinical areas like tracking epidemics and for improving healthcare workflows.

We found significant variations in knowledge levels of AI and comfort levels pertaining to AI use in health care based on sociodemographic, digital literacy, and the number of health conditions. Our results indicate that men, noncitizens, higher-income respondents, and respondents with greater digital health literacy exhibited higher odds of reporting comfort with AI use in health care and with the use of personal health data for AI. Older adults (65+ y) demonstrated higher comfort with AI use in health care, while younger (25–34 y) and middle-aged (35–54 y) adults were less comfortable with AI using their personal data, especially without consent.

Compared to Asian-origin respondents, White and Other racial groups had significantly lower comfort levels across models, while Black or African-origin respondents were notably less comfortable, only when personal health data were used for AI applications without consent. This finding suggests that lower comfort among Black respondents is not a general discomfort with AI but rather a heightened sensitivity to nonconsensual data use, thus underscoring the critical importance of transparency and opt-in data policies to foster trust among minority groups. Our findings also indicated that noncitizens exhibited higher comfort levels with AI use in health care as compared to Canadian citizens. The observed higher comfort among Asian-origin and noncitizen respondents and lower comfort among Black respondents suggests that broader cultural or experiential factors may influence attitudes toward AI in health care. Future qualitative or mixed-methods studies are needed to explore these factors. Prior research has shown that historical experiences, prior exposure to technology [], and privacy concerns shape levels of trust in health technologies, particularly among minority populations [,]. Additionally, there is also some evidence that suggests that willingness to share personal health data for AI use depends strongly on the institution collecting the data and its intended purpose [].

While digital health literacy improved comfort levels with use of AI, we also found that individuals with multiple health conditions were more accepting of AI when personal health data use was consensual. Individuals with multiple chronic conditions may have greater familiarity with varied health technologies and tools like wearables due to their increased prevalence [-], promoting appreciation for AI’s potential in managing complex conditions.

Implications

One of the critical challenges in deploying AI solutions in health care is ensuring fairness and reducing algorithmic bias, which often arises from unrepresentative training datasets [-]. To build AI models that produce accurate, equitable, and generalizable outcomes, they must be trained on large, diverse, and high-quality datasets that reflect the complete range of patient demographics and health conditions []. Our findings point to the importance of placing explicit patient consent at the core of all efforts in developing AI solutions.

Health care institutions and policymakers must establish standardized protocols for obtaining patient consent for AI use, ensuring that data collection aligns with ethical and legal frameworks (eg, HIPAA [Health Insurance Portability and Accountability Act] in USA, PIPEDA [Personal Information Protection and Electronic Documents Act] in Canada, and GDPR [General Data Protection Regulation] in Europe) [,]. These policies must define whether patient data is being used for research, commercial development, or clinical decision-making. They must also clarify how long the data will be stored, who can access it, and whether patients have the right to withdraw consent at any time []. Without well-defined guidelines, the risk of unauthorized data usage and breaches increases, undermining public confidence in health care AI.

Even when patients provide consent, strong privacy protections must be in place, particularly when data are pooled across multiple health systems. There is a growing concern about deidentification and whether anonymized health data can still be reidentified using advanced AI techniques []. To mitigate these risks, health care institutions must implement privacy-preserving solutions, such as federated learning, where AI models are trained across decentralized data sources without transferring raw patient data [,]. Blockchain-based consent management can offer a secure way for patients to track and manage their data access, while strict data governance frameworks are essential to ensure AI developers use health data more responsibly.

This study shows that comfort with AI in health care is strongly influenced by sociodemographic factors and digital literacy. Individuals who trust AI and understand how it works are more likely to support AI-driven health care applications, while those with privacy concerns or lower digital literacy may resist AI use in health care, specifically when personal health data is used without consent. Addressing these concerns through educational initiatives, transparent policies, and patient engagement strategies can help build public confidence in AI solutions in health care.

Our findings also indicate that respondents were significantly less comfortable with AI use when personal health data were used without explicit consent. This highlights a crucial ethical dilemma—even if deidentified, patient data still carries risks if used without oversight or patient involvement. Future research and policy discussions should explore how much control patients should have over their deidentified data, what level of transparency AI developers must provide to patients, and how AI models trained on patient data should be evaluated for fairness and accountability. Algorithmic bias, as seen in lower comfort among certain racial groups like African Americans, especially when consent is absent, could exacerbate health care disparities if AI models are trained on unrepresentative datasets []. Mitigating algorithmic bias through diverse dataset inclusion and continuous performance monitoring can help reduce disparities [,]. Accountability in clinical decision-making is critical to ensure that AI supports, rather than overrides, clinician judgment, while prioritizing patient autonomy in data use strengthens trust [,]. These ethical challenges highlight the need for aligning AI deployment with patient expectations and equitable outcomes.

To build trustworthy AI-enabled health care solutions, policymakers and health administrators need to design targeted public awareness campaigns, co-developed with patient advocates, that clearly explain AI’s clinical role and how it uses patient data []. In addition, implementing opt-in consent policies [,] can help alleviate patient fears about misuse of their data. Culturally tailored digital literacy programs can also help boost patient confidence about AI use in health care. Beyond patient engagement, standardized bias audits for all clinical AI tools [], while establishing patient review boards or advisory committees to gather patient feedback and assess ethical implications before deployment can be effective [].

Limitations

This study has several important limitations. First, the cross-sectional design captures public attitudes at a single point in time. The survey was done at the end of 2023 in Canada, when new generational AI technologies were still emerging. As advances in AI technologies and public awareness evolve, attitudes may change, making longitudinal studies necessary. Second, the self-reported nature of the data may introduce bias. Respondents could have misestimated their AI knowledge and comfort levels due to social desirability. Additionally, self-reported digital literacy may not accurately reflect actual proficiency in digital health technologies. Third, the study explored respondent attitudes toward AI, without assessing whether they had any prior exposure to any AI health care tools, such as chatbots, etc. Fourth, though we had a fairly large sample size, it may not be representative of the general population, restricting the generalizability of results. Specifically, our sample included relatively small proportions of noncitizens (4.68%) and Black or African-origin respondents (3.66%), which reduces statistical power for these subgroups. Consequently, subgroup findings should be interpreted with caution. Fifth, our operationalization of chronic health conditions as a composite count is a measurement limitation. Different health conditions, based on their nature and severity, could influence varied levels of technological use and engagement. Future research could explore how specific health conditions, and their nature and severity, influence attitudes toward AI. We also acknowledge possible nonresponse bias, as over 3226 cases were removed due to missing answers on AI-related questions. This reduction in analytic sample size may have excluded individuals with systematically different attitudes toward AI. Although weighting adjustments were used to correct for this, some bias may remain, as the weighting cannot account for unobserved factors. For instance, respondents who systematically avoided answering AI-related questions may hold strong, unmeasured attitudes such as anxiety or mistrust toward AI, which could bias our analytic sample. Finally, while the study examined broad sociodemographic and health factors, it did not delve into more specific determinants of AI trust, such as ethical concerns, data security apprehensions, or past experiences with health care technologies. Future research could explore these in greater detail to better understand the specific reasons behind patient acceptance of AI in health care. Additional investigation through qualitative or mixed-method studies could throw light on specific nuances that shape the patient attitudes toward AI use in health care.

Conclusions

In conclusion, this study documents moderate levels of knowledge and comfort levels of the public regarding AI use in health care. Further, it highlights how sociodemographic characteristics, digital literacy, and health conditions are associated with public knowledge and comfort levels regarding AI use in health care. Our findings suggest significant socioeconomic disparities around the comfort levels with AI use in health care, while concerns persist around AI use without patient consent. These findings highlight the importance of transparent policies, patient education, and ethical data governance to improve public trust in AI-driven health care.

WASHINGTON, Dec 5 (Reuters) – Billionaire entrepreneur Elon Musk’s social media platform X Corp has sued one of its former senior engineers for allegedly stealing proprietary source code to launch a competing business.

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Yue previously served as X’s principal software engineer on the company’s infrastructure optimization and performance team. Her LinkedIn profile shows she worked for the platform for more than a decade.

IOP Systems and X did not immediately respond to requests for comment. Yue could not be reached for comment.

X was formerly known as Twitter before Musk bought the platform in 2022 for $44 billion.

The lawsuit alleges that weeks after Musk’s acquisition, Yue took advantage of changes in management to “willfully and maliciously” copy millions of lines of confidential code and internal tools from her company laptop to external drives.

X fired Yue in late 2022 after she publicly questioned Musk’s new return-to-office policy at X and encouraged employees not to resign but instead to be fired, according to the lawsuit. X alleges Yue “orchestrated” her own ouster to raise her profile.

X claims the code Yue allegedly took with her was designed to optimize system performance and reduce operating costs — technology that the company says is central to competition.

The lawsuit alleges Yue used the code to form IOP Systems, which markets performance-monitoring software. X Corp alleges IOP’s offerings mirror the proprietary tools Yue helped develop while at X.

IOP’s website says its team “met while working for Twitter, where we saved the company over $100,000,000 over 5 years.”

X seeks a court order to prevent any further alleged use or disclosure of its trade secrets, and an order requiring the return of copied materials. The lawsuit also seeks unspecified monetary damages.

The case is X Corp v. Yao Yue et al, U.S. District Court, Northern District of California, No. 3:25-cv-10423.

For plaintiff: Jeanine Zalduendo and William Odom of Zweiback, Fiset & Zalduendo

For defendants: No appearances yet

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Introduction

Background

Inflammatory bowel disease (IBD) is a chronic, immune-mediated inflammatory condition of the gastrointestinal tract, comprising ulcerative colitis, Crohn disease, and unclassified types []. The global prevalence of IBD has been rising [], which results in notable economic and health care burdens []. With enhanced diagnostic capabilities and rapid urbanization, the incidence of IBD has significantly risen in China, making it the country with the highest prevalence in Asia []. By 2025, the number of individuals affected by IBD could reach 1.5 million in China []. The highest incidence of IBD is among adolescents and young adults [].

Currently, IBD is a lifelong condition with no definitive cure. Manifestations such as repeated diarrhea, fecal blood, stomach ache, and severe tiredness greatly affect the life quality of adolescents and young adults [], with self-management behaviors being crucial in enhancing life quality and disease prognosis []. Self-management behaviors encompass patient actions aimed at sustaining and enhancing their health via various self-guided actions, covering areas such as medical, emotional, and role management [].

The adolescent and young adult phase represents a critical transition from childhood to adulthood, characterized by substantial physiological and social role transformations []. Young individuals with IBD encounter dual challenges: managing their medical condition while adapting to social role changes []. Consequently, researchers have highlighted that self-management behaviors of adolescents and young adults with IBD need to be improved [-]. Therefore, effective interventions are urgently needed to enhance self-management behaviors in this population.

However, existing interventions for adolescents and young adults with IBD often concentrate on isolated aspects of self-management and demonstrate considerable heterogeneity in results []. Moreover, these interventions [], which are typically led by psychologists, do not align with China’s clinical practice. In China, clinical nurses primarily assume responsibility for patient self-management. Consequently, it is critically important to develop an all-encompassing and effective program for self-management behaviors of adolescents and young adults with IBD, particularly within a nurse-led clinical environment.

The formation and sustainability of self-management behaviors are underpinned by underlying motivational mechanisms. The self-determination theory [] serves as a pivotal framework in behavioral studies, playing a crucial role in predictive model construction and intervention design []. This theory highlights that fulfilling basic psychological needs (competence, autonomy, and relatedness) is indispensable for fostering motivation and sustaining behaviors [].

Based on the self-determination theory, our research team conducted a preliminary study [] on the influencing factors of self-management behaviors in adolescents and young adults with IBD. The study [] revealed that perceived social support would influence self-management behaviors through the mediating effects of basic psychological needs and emotional issues, indicating that enhancing perceived social support, satisfying basic psychological needs, and alleviating emotional issues were crucial for improving self-management behaviors. To identify effective strategies for these improvements, we conducted a systematic review of evidence [] in self-management interventions for this population. The review found that multicomponent interventions were the most effective approach. Health education was necessary to increase knowledge and satisfy the need for competence; peer support could significantly enhance perceived social support and satisfy the need for relatedness; group-based mindfulness training could effectively relieve emotional problems; and remote interventions were shown to improve adherence to intervention among adolescents and young adults. In addition, solution-focused intervention [], which complements self-determination theory by addressing the basic psychological needs [], has been commonly applied in nursing in the form of short-term groups to enhance self-management behaviors among adolescents and young adults [].

Building on our preliminary study [] and systematic review [], we designed a multicomponent intervention program tailored to enhance self-management behaviors in adolescents and young adults with IBD. This program was delivered through short-term remote group sessions and integrated health education, solution-focused intervention, peer support, and mindfulness training to address the basic psychological needs underlying self-management behaviors, thereby promoting the initiation and maintenance of self-management behaviors.

Objectives

This research primarily aimed to evaluate the effectiveness of this intervention program over standard care in fostering self-management behaviors among adolescents and young adults with IBD. The ancillary goals included assessing its effectiveness in improving the perceived social support and basic psychological needs, diminishing levels of anxiety and depression, and lessening disease activity in this group.

Methods

This study was implemented in accordance with the predefined trial protocol [] and was reported according to the CONSORT (Consolidated Standards of Reporting Trials) reporting guidelines [].

Study Design and Setting

Conducted between July 2024 and January 2025, this research entailed a double-center, single-blind, 2-arm randomized controlled trial in gastroenterology units of 2 tertiary hospitals (1 pediatric hospital and 1 general hospital) in Chongqing, China. Chongqing stands as a municipality under direct administration and a central national city in China.

Participants

Inclusion criteria were diagnosis of ulcerative colitis or Crohn disease [], age ranging from 13 to 24 years [], and ability to provide informed consent and express oneself clearly. Exclusion criteria were having severe intellectual impairment; pregnancy; history of cancer or active cancer diagnosis; currently receiving psychiatric medications, therapy, or other psychological intervention; and refusal to participate. Withdrawal criteria were voluntary withdrawal for personal reasons, accompanied by an exit interview to elucidate the reasons for withdrawal; and loss of contact.

Informed Consent and Baseline Assessment

A researcher (DLW) recruited participants from inpatients at the gastroenterology wards of the 2 hospitals in July 2024. Eligible patients were identified by reviewing daily admission lists and approached directly in their wards. The researcher provided a verbal explanation of the study and obtained written informed consent from participants or guardians. For participants younger than 18 years, parental consent was required before obtaining the adolescents’ consent. The recruiting researcher was not involved in the delivery of the intervention.

The baseline assessment was administered via a unique web link to the questionnaire hosted on the Wenjuanxing platform (a Chinese online survey tool compliant with data privacy regulations) within 24-48 hours after obtaining informed consent. Following completion of research ethics and survey administration training, the researchers (JJH and XW) conducted a baseline assessment, which included (1) collection of general information, including age, gender, residence, ethnicity, annual household income, current educational background, main caregiver, disease type and duration, and surgical history; and (2) assessment of outcome variables, as described in “Outcome Assessment” section.

Sample Size

The sample size was calculated using PASS software (version 16.0; NCSS Limited Liability Company), with parameters derived from a related previous study []. Specifically, that study [] reported a mean difference of 10.1 in self-management behavior scores between the 2 comparison groups, with a corresponding pooled SD of 5.54. Setting a significance level (α) of .05 (2-tailed) and a desired statistical power of 0.8 (80%), the initial calculated sample size was 47 participants. After accounting for a projected dropout rate of 19% (9/47), the final minimum sample size was determined to be 56 participants, with no fewer than 28 individuals in each group (intervention group and control group).

Randomization and Blinding

Following enrollment, participants were assigned sequential numbers. A researcher independent of the study team then randomly allocated them to the control and intervention groups at a 1:1 ratio, using random numbers generated by the RAND function in Excel software (Microsoft Corp). To ensure allocation concealment, allocation results were stored in sequentially numbered, sealed envelopes maintained by an independent research assistant external to the research team and opened only at the time of intervention initiation. Participants, the recruiter, outcome evaluators, and the data analyst were blinded to group assignments.

Intervention

Control Group

Routine care was provided to the control group, including face-to-face health education during hospitalization and at discharge, a telephone follow-up within 1 week of discharge, and real-time doctor-patient communication via WeChat (a widely used social media app in China). While not formally standardized across all settings, this communication method aligns with local clinical practices in our region for maintaining postdischarge engagement.

Intervention Group

The intervention group received the remote intervention program in addition to routine care. To develop this program, a stakeholder workshop was organized. For this workshop, 2 adolescents and young adults with IBD (aged 17 years with a 3-year disease history and 21 years with a 5-year disease history, respectively) and 13 health providers (see Table S1 in for details) were invited to discuss and revise the draft program, culminating in the finalization of a multicomponent remote group intervention program. In addition, data on the health care providers’ judgment bases and their familiarity were collected (Tables S2 and S3 in ). Based on these judgment bases and familiarity levels, we further calculated the authority coefficient of the health care providers’ judgments. The specific calculation principles and results are provided in the “Health Care Providers’ Judgments” section of .

The intervention program is detailed in Table S4 in . This program consisted of 9 weekly sessions facilitated through a remote conferencing platform (Tencent Meetings software; Tencent Holdings Limited). This 9-week duration aligned with the semester vacation of Chinese students, which was expected to increase their participation enthusiasm. With the exception of the initial and final weeks, which focused on starting and ending the program, every weekly session comprised these components:

1. Health education: This component covered medication, dietary management, physical exercise, disease monitoring, vaccination, and home care procedures. Its objective was to enhance self-management knowledge and satisfy the need for competence.
2. Solution-focused intervention: This involved goal-setting discussions, exception-seeking questions, scaling questions, miracle questions, and relationship-oriented questions, aiming to comprehensively boost the satisfaction of basic psychological needs.
3. Peer support: Participants engaged in discussions and shared their experiences and insights, fulfilling the need for relatedness. Volunteers from local patient organizations were also invited to share their stories, encouraging and motivating participants to open up.
4. Mindfulness training: This component aimed to relax the emotion and enhance the perception of internal and external resources.

Regulating Quality

To guarantee the effectiveness of the program’s execution within the intervention group, these steps were implemented:

Preparation of Intervention Materials

To aid participants in fully grasping the program, a uniform manual, a tailored canvas bag, and a pen (illustrated in Figure S1 in ) were created and disseminated.

Implementation of the Intervention

The nurse (YFZ) received training from the psychological counselor (YYC). The counselor participated throughout the intervention process to provide quality supervision and guidance.

The intervention adopted a group discussion approach. Using the online conferencing Tencent Meetings software, participants were randomly divided into smaller groups of 2-3 members. After the group discussions, a collective sharing session was held to enhance engagement.

Reinforcement of Intervention Effects

Relevant homework assignments were assigned to reinforce and solidify the effects of the intervention.

After each intervention activity, adolescents were required to complete a feedback scale to rate their satisfaction on a scale of 1-5.

For participants unable to attend sessions in real time, the intervention was documented via video recording of the full group session. Researcher YFZ supervised these participants to ensure that they viewed the recorded videos within 1 week of the session.

Outcome Assessment

Information was gathered by researchers (JJH and XW) through the self-reporting questionnaire on the Wenjuanxing platform, with the exception of disease activity, which underwent external evaluation via the electronic medical record system and phone interviews.

Measurement of the Main Outcome Indicator: Self-Management Behavior

We used the Self-Management Behavior Scale of Inflammatory Bowel Disease, developed by Chinese scholars [], to assess the self-management behaviors of the participants. The scale encompasses 7 dimensions: medication management, dietary practices, disease monitoring, emotional regulation, physical exercise, daily life, and resource utilization, and it comprises 36 items. Responses are gauged on a 5-point scale, ranging from 1 (never) to 5 (always). The Cronbach a coefficient of the scale was 0.945 in the original study and 0.941 in this study. Chinese scholars commonly use this scale to evaluate self-management in patients with IBD [].

Measurement of Secondary Outcome Indicators

Basic Psychological Needs

This study used the Chinese version of the Basic Psychological Needs Satisfaction Scale [], adapted from the original one []. This version contains 9 items and 3 dimensions, namely, autonomy, competence, and relatedness. The rating for each item ranges from 1 (strongly disagree) to 7 (strongly agree). The Cronbach a coefficient of this scale was 0.86 in the original study and 0.941 in this study. This version of this scale has been widely used [].

Perceived Social Support

This study used the Chinese version of the Perceived Social Support Scale [], adapted from the original one []. The scale consists of 12 items divided into 3 dimensions: family support, friend support, and other support, and is assessed on a 7-point scale from 1 (strongly disagree) to 7 (strongly agree). The Cronbach a coefficient of the scale was 0.88 in the original study and 0.943 in this study. This scale has been widely used [].

Anxiety

The Generalized Anxiety Disorder 7-item Scale (GAD-7) was used to assess the severity of anxiety over the past 2 weeks. Comprising 7 elements, this item is evaluated on a scale ranging from 0 (not at all) to 3 (almost daily). The total score of GAD-7 ranges from 0 to 21, with score ranges interpreted as follows: 0-4 points indicate no significant anxiety symptoms, 5-9 points denote mild anxiety symptoms, and a score of ≥10 points indicates the generalized anxiety symptoms []. The Chinese version of the GAD-7 has been widely used in clinical practice []. The Cronbach a coefficient of this scale was 0.937 in this study.

Depression

The Patient Health Questionnaire-9 (PHQ-9) was used to assess the level of depression in the past 2 weeks. It contains 9 items, which are scored on a scale from 0 (not at all) to 3 (almost every day). The total score of the PHQ-9 ranges from 0 to 27, with established interpretive criteria: 0-4 points indicate no significant depressive symptoms, 5-9 points denote mild depressive symptoms, and a score of ≥10 points is indicative of moderate to severe depression symptoms []. The Chinese version of the PHQ-9 is a reliable measure of depressive symptoms in clinical practice []. The Cronbach a coefficient of this scale was 0.920 in this study.

Disease Activity Level

For participants with Crohn disease, the Pediatric Crohn’s Disease Activity Index was applied to those younger than 18 years, while the Crohn’s Disease Activity Index was used for those aged 18 years and older. For participants with ulcerative colitis, the Pediatric Ulcerative Colitis Activity Index was used for those younger than 18 years, and the Simple Clinical Colitis Activity Index was used for those aged 18 years and older. Using these measurements, the severity of disease activity was categorized into remission, mild, moderate, or severe [].

Evaluation Schedule

Outcome indicators of participants were assessed at baseline (T0), immediately after the intervention (T1), and 12 weeks after the intervention (T2). For validity, an interim analysis was conducted at T1: no significant differences in primary or secondary outcomes would have resulted in a decision to stop T2 follow-up; if differences existed, results remained confidential until all data collection was complete (in line with the blinded protocol), with details available in the study protocol [].

Statistical Analysis

Statistical analyses were conducted using SPSS software (version 26.0; IBM Corp). To examine categorical data across 2 groups, either the chi-square test or the Fisher exact test was used, with findings displayed in terms of frequencies and percentages. Normality tests were performed on continuous variables to determine the suitable statistical techniques. Information showing a normal distribution underwent analysis via the t test and was presented as mean (SD). In contrast, data that did not follow a normal distribution were evaluated using the rank sum test and presented as median (IQR).

For normally distributed data assessed at multiple time points within a group, mixed-design analysis of variance was used. Effect sizes were presented as partial eta-squared (η² ). The value of η² ranges from 0 to 1 and can be interpreted as small (η² ≥0.01), medium (η²≥0.06), and large effects (η²≥0.14) []; for non–normally distributed data, the Friedman test was applied, with Bonferroni correction used for post hoc multiple comparisons. The significance level (α) was set at .05. For Bonferroni correction, the adjusted significance threshold was calculated as 0.05 divided by the number of comparisons (n=3), resulting in a corrected statistical significance level of P<.017. Subgroup analyses were not conducted due to the small sample size in this study.

A Little’s Missing Completely At Random test was performed to evaluate the missing mechanism (χ²₁₄=10.82; P=.63), confirming that the data were missing completely at random. Missing data were addressed through multiple imputation methods. The analysis of this study adhered to the principles of intention-to-treat analysis.

Ethical Considerations

Approval for the research was granted by the ethics review boards of the Children’s Hospital of Chongqing Medical University and Chongqing General Hospital (approval numbers: file nos. 2023,395 and KYS2024-008-01). No ethical exemption was applied. Written informed consent was obtained from all participants (with guardians providing consent for those younger than 18 years), and the informed consent forms are available in . No secondary analysis was planned, with ethics approval for no extra consent. Data were deidentified (unique codes) and stored encrypted. No participant compensation was provided. No identifiable images were included; future use requires consent and form uploads.

Result

Overview

Initially, 91 potential participants were identified, with 17 excluded: 2 ineligible for failing to meet the inclusion age, 3 ineligible due to unconfirmed diagnosis, and 12 declining participation for personal reasons. As a result, 74 participants were recruited, with 37 assigned to the intervention group and 37 to the control group. Of the participants, 74 (100%) completed the evaluation at T0; 72 (97.3%) completed the evaluation at T1, with 2 cases of missing data (2.7% missing rate); and 69 (93.2%) completed the evaluation at T2, resulting in 5 cases of missing data (6.8% missing rate). A flow diagram of the study is shown in . No important harms or unintended effects were observed in either group.

In the intervention group, the mean real-time participation rate during the 9 online intervention sessions was 79.52% (95% CI 66.8%-92.2%), while the recorded video-viewing rate was 20.48% (95% CI 8.1%-32.9%). The satisfaction score was mean 4.97 (SD 0.08, 95% CI 4.94-5.00) on a 5-point scale.

Baseline Characteristics

The age of the participants was mean 18.95 (SD 2.96) years. Males constituted 71.62% (53/74) of the sample. displays the sociodemographic details and clinical traits of the participants, revealing no significant difference between the intervention and control groups at baseline.

Effects of the Intervention on the Primary Outcome

As shown in , regarding self-management behaviors, a significant time × group interaction was observed (F_{interaction effect}=8.339; P<.001); between-group comparisons showed no difference at T0 (95% CI –12.728 to 5.539; P=.435, η²=0.008) but significant superiority of the intervention group at T1 (95% CI –24.370 to –8.982; P<.001, η²=0.206) and T2 (95% CI –22.594 to –5.784; P=.001, η²=0.136); and within-group analyses revealed no changes in the control group (P=.16, η²=0.050) but significant differences in the intervention group (P<.001, η²=0.426). For detailed within-group comparisons across different time points, see Table S5 in . The trend of these results is illustrated in A. For the analysis of the scores across various dimensions of self-management behaviors, refer to Table S6 in .

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Effects of the Intervention on Secondary Outcomes

Effects of the Intervention on Perceived Social Support

As shown in , regarding perceived social support, a significant time × group interaction was observed (F_{interaction effect}=3.264; P=.04); between-group comparisons showed no difference at T0 (95% Cl –9.030 to 0.868; P=.105, η²=0.036) but significant superiority of the intervention group at T1 (95% CI –13.932 to –4.068; P=.001, η²=0.155) and T2 (95% CI –11.890 to –1.407; P=.014, η²=0.082); and within-group analyses revealed no changes in the control group (P=.76, η²=0.008) but significant differences in the intervention group (P=.001, η²=0.190). For detailed within-group comparisons across different time points, see Table S5 in . The trend of these results is illustrated in B. For the analysis of the scores across various dimensions of perceived social support, refer to Table S7 in .

Effects of the Intervention on Basic Psychological Needs

As shown in , regarding basic psychological needs, no significant time × group interaction was observed (F_{interaction effect}=1.231; P=.30); between-group comparisons showed no difference at T0 (95% CI –6.173 to 0.930; P=.146, η²=0.029) but significant superiority of the intervention group at T1 (95% CI –8.323 to –1.569; P=.005, η²=0.106) and T2 (95% CI –7.720 to –0.172; P=.04, η²=0.057); and within-group analyses revealed no changes in the control group (P=.98, η²=0.001) but significant differences in the intervention group (P=.049, η²=0.081). For detailed within-group comparisons across different time points, see Table S5 in . The trend of these results is illustrated in C. For the analysis of the scores across various dimensions of basic psychological needs, refer to Table S8 in .

Effects of the Intervention on Anxiety

The Mann-Whitney U test was conducted to compare anxiety scores between the 2 groups at different time points, with the results summarized in . At T0, there was no significant difference detected among the groups (P=.75, z=–0.321). At T1 and T2, the intervention group demonstrated statistically lower scores than the control group (P=.04, z=–2.096; P=.007, z=–2.69). Within-group comparisons revealed that the control group’s anxiety scores exhibited a statistically significant overall difference (P=.007, χ²₂=9.894), with post hoc analysis indicating that the score at T2 was significantly lower than that at T0 (P<.017). For the intervention group, anxiety scores also showed a statistically significant overall difference (P<.001, χ²₂=32.463), with post hoc analysis demonstrating that scores at both T1 and T2 were significantly lower than that at T0 (P<.017). The trend of these results is illustrated in D.

Effects of the Intervention on Depression

The analysis of depression scores is shown in . At T0, the 2 groups showed no significant difference (P=.92, z=–0.098). At T1 and T2, the intervention group demonstrated statistically lower scores than the control group (P=.048, z=–1.981; P=.03, z=–2.115). Within-group comparisons revealed that the control group’s depression scores exhibited a statistically significant overall difference (P=.03, χ²₂=6.764). However, the post hoc analysis showed no statistically significant differences between time points in the control group (P>.017). For the intervention group, depression scores also showed a statistically significant overall difference (P<.001, χ²₂=15.228), with post hoc analysis demonstrating that scores assessed at T1 and T2 were markedly less than that at T0 (P<.017). The trend of these results is illustrated in E.

Effects of the Intervention on Disease Activity

Disease activity between the 2 groups was evaluated using the Mann-Whitney U test, as detailed in . Findings showed negligible variance in disease activity between the groups at T0 and T1 (P=.44, z=–0.769; P=.16, z=–1.403). At T2, a higher percentage of participants in the intervention group experienced remission than those in the control group, showing statistically significant differences (P=.03, z=–2.231).

Discussion

Principal Findings

Self-determination theory has been widely validated for improving self-management behaviors in other populations with chronic diseases [,]. A key innovation of this study was its first application of this theory to adolescents and young adults with IBD, offering a novel theoretical framework for clinical interventions targeting this population. Based on the mechanisms underlying the formation and sustainability of self-management behaviors [], this study developed a remote multicomponent program, integrating health education, solution-focused intervention, peer support, and mindfulness training This intervention program showed significant effects in enhancing self-management behaviors, strengthening perceived social support, and fulfilling basic psychological needs among adolescents and young adults with IBD, while also mitigating their anxiety, depression, and disease activity. Notably, unlike traditional in-person intervention, this remote program could offer greater flexibility. The favorable real-time participation rate and satisfactory feedback score in the intervention group indicated that the program was well received by participants.

Regarding self-management behaviors, the intervention group demonstrated superiority over routine care, highlighting that the intervention program should serve as a valuable and beneficial complement to routine care. Routine care primarily relies on one-way health education. As a complex, multidimensional construct (encompassing disease, emotional, and role management), self-management behaviors cannot be fully improved by routine care’s typical one-way health education []. Critically, most self-management intervention studies have been led by specialized psychotherapists [], rendering them unsuitable for nurse-led clinical settings. Although this study used a multidisciplinary and multicomponent intervention, its overall nurse-led approach could enhance clinical feasibility and offer insights for regions with similar clinical contexts.

In perceived social support, the intervention group exhibited a significant advantage over the control group. This advantage in the intervention group could be plausibly attributed to the intervention’s multicomponent design. Unlike extant literature [] that predominantly used peer support to modulate psychological outcomes in patients with IBD, this study innovatively integrated peer support with solution-focused intervention, transcending passive reciprocal assistance to proactively cultivate participants’ capacity to identify, mobilize, and optimize inherent support resources within their lived contexts.

In addition, this study revealed that the scores of competence and relatedness (2 dimensions of basic psychological needs) in the intervention group were higher than those in the control group at T1 and T2 (see Table S8 in ). However, the autonomy dimension did not achieve significance either within groups or between groups at all time points, as elaborated in Table S8 in . Although theoretical literature [] posited that solution-focused intervention could enhance the satisfaction of basic psychological needs, its practical application should be contextualized within specific cultural backgrounds. Within an Asian cultural context, parental authority and overprotection often hinder the development of adolescents’ autonomy []. Against this cultural backdrop, the autonomy of participants in this study proved challenging to foster in the absence of parental involvement. From the perspective of self-determination theory, this study framed autonomy around attaining self-independence. Notably, the program might not account for adolescents’ potential to view “relying on parents” as an autonomous choice. Future research should thus reframe objectives to explore how adolescents use parental support to meet autonomy needs, rather than solely emphasizing self-independence.

Although the intervention program outperformed routine care in reducing anxiety and depression in the participants, within-group analyses showed that the control group also had significantly lower anxiety scores at T2 than at T0. This finding implied that routine care had a certain positive impact on emotion. Alternatively, it could be inferred that the potential for self-growth among adolescents and young adults with IBD was consistent with other research [] that reported posttraumatic growth trends in this population. This observation corroborated the use of a solution-focused approach, which guided participants to recognize intrinsic resources (inherently present in participants, with the intervention facilitating awareness of personal strengths). Furthermore, this study advanced posttraumatic growth theory from phenomenological description to intervention-based empirical validation in this population, providing an entry point for investigating the mediating mechanisms of the disease-related stress and self-growth pathway.

Finally, there were no significant changes in disease activity levels at T1; however, a significant improvement was observed at T2, providing empirical support for the influence of mental health on disease activity, consistent with “gut-brain axis” theory []. This result suggested that the psychological intervention did not yield immediate disease benefits and sustained engagement was needed to modulate brain-gut cross talk, clinically guiding health care providers and patients to set realistic expectations for long-term adherence. Notably, while other study [] has also reported that adding psychological interventions to routine care effectively alleviates disease activity, these interventions were predominantly led by specialized psychologists. In contrast, the nurse-led model of this study could render gut-brain axis-informed care accessible in regions with limited access to psychologists.

Limitations

However, this study still has some limitations. The long-term effectiveness of this study remains to be further verified, as follow-up was limited to 12 weeks. It is recommended to conduct long-term follow-up to determine whether the intervention effect is sustainable in the long run. Furthermore, the sample in this study mainly consisted of individuals with Crohn disease (65/74, 87.84%), males (53/74, 71.62%), and urban populations (62/74, 83.78%). Although this is in line with the epidemiological characteristics of IBD in China [], the imbalance limits generalizability. Efficacy in subgroups such as rural residents or patients with ulcerative colitis remains untested, as these groups may face unique barriers (eg, limited access to remote resources in rural areas) affecting outcomes. In addition, while the sample size calculation confirmed sufficient statistical power for the primary outcomes, the modest sample size may hinder detection of small but clinically meaningful effects (eg, the autonomy dimension of basic psychological needs).

Conclusions

Based on the self-determination theory, this study developed a short-term, group-based, remote, and multicomponent intervention program, integrating health education, peer support, solution-focused intervention, and mindfulness training. The program demonstrated improvements in self-management behaviors, perceived social support, and basic psychological needs among adolescents and young adults with IBD, while also alleviating their anxiety, depression, and disease activity. Theoretically, this study validated the application of a combination of multiple intervention components under the guidance of self-determination theory in adolescents and young adults with IBD. Practically, it was shown that the nurse-led remote intervention was feasible and accessible. Future research should verify the program’s long-term effectiveness and expand to more balanced samples to enhance generalizability; optimizing the intervention to address unmet autonomy needs could further boost its clinical use.

Introduction

Chronic urinary conditions, such as benign prostatic hyperplasia (BPH), necessitate ongoing patient self-management, akin to other chronic diseases such as hypertension, diabetes, and asthma. Despite this need, there is a notable lack of tools enabling patients to monitor and manage urinary symptoms autonomously at home. This absence increases the risk of symptom progression and the onset of secondary urinary disorders. Even in cases where pharmacological treatments are suboptimal, patients lack effective methods for self-monitoring [].

Postoperative urinary complications can also occur following interventions for prostate enlargement. The inability of patients to self-assess their condition may result in delayed recognition of issues, potentially necessitating urgent interventions, such as urethral catheterization []. Traditional methods, such as the use of a urination diary, which requires patients to record their symptoms for several days using paper and a measuring cup, are cumbersome and often impractical. If these records are misplaced, patients lose the ability to accurately track their urinary habits, and health care professionals face additional burdens in manually calculating metrics such as daily and nocturnal urine output.

The integration of digital therapeutics presents significant benefits, including the absence of toxicity and adverse side effects, minimal costs, and continuous real-time management []. These tools facilitate 24-hour monitoring of patient status and allow for personalized patient analytics by empowering patients to actively participate in data collection and management. Individuals can actively engage in self-health monitoring and retrieve evaluation results before consulting medical personnel, reducing time consumed during in-person visits to clinics and overcoming geographic barriers when physical presence is limited due to infection or regional availability of medical services.

As an attempt to address the needs of both clinicians and patients for a personalized device to measure and monitor voiding-related outcomes during and after treatment, an acoustic uroflowmetry was incorporated into a mobile app [,]. Uroflowmetry, which is a pivotal test for the evaluation of lower urinary tract function and voiding patterns during diagnosis and treatment, requires the patient to visit the hospital and undergo testing in a controlled setting. Moreover, single measurements obtained in a hospital setting may not reflect a patient’s usual voiding patterns. Acoustic uroflowmetry using mobile apps provides a practical alternative to replace conventional methods, allowing repeated measurements in familiar home environments, enabling remote monitoring as well as accessibility for patients, and improving efficiency in clinical practice.

However, despite worldwide interest and the development of similar tools, no study has generated data on the comparative outcome between mobile uroflowmetry and in-office measurements for surveillance of posttreatment change. This study compares an acoustic analysis–based uroflowmetry, which calculates urine volume by recording sounds with a smartphone, with a conventional physical urinary flow test machine in patients undergoing surgery for BPH. The aim is to evaluate whether traditional in-office tests can be effectively replaced.

Methods

Ethical Considerations

This study received approval from the institutional review board of Seoul National University Bundang Hospital (B-2207-769-305). All data analyzed in this research were anonymized, and informed consent was obtained from all patients participating. The participants received monetary compensation of South Korean ₩ 100,000 (approximately US $67.90) for hospital visitation and traveling fees during the entire study. To ensure privacy, personal identifiers were fully removed, and the analysis was conducted anonymously. All procedures adhered to the relevant ethical guidelines and regulations. A STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist was submitted as supplementary material ().

Study Design and Population

Patients diagnosed with BPH planning to undergo surgery (transurethral resection of prostate [TURP]) at 3 tertiary institutions (Seoul National University Bundang Hospital, Ewha Womans University Medical Center, and Kyung Hee University Medical Center), who were older than 20 years, were screened for eligibility and enrolled after obtaining informed consent. This study was designed as a prospective pilot observational study in a single cohort without a control to validate the efficacy of mobile uroflowmetry measurements compared to conventional methods conducted in an office. On the basis of previous literature [,] suggesting a moderate correlation (expected r=0.6), a minimum of 20 patients would provide 80% power to detect a statistically significant correlation at a 2-sided α level of .05. To improve the precision of the correlation estimate, allow for potential measurement failure or incomplete data, and support the feasibility of future definitive studies, we increased the target enrollment to 40 patients. During the first outpatient visits, patients planning to undergo surgery for BPH were recommended for screening and enrollment, if eligible. After initial study enrollment, individual mobile devices with the app installed were distributed for preoperative evaluation of voiding patterns and parameter measurements for at least 72 to 96 hours before surgery. In-office measurements were also conducted for comparison. Additional app measurements were taken for the same 4-day periods after 2, 6, and 12 weeks of surgery and compared to conventional uroflowmetry measurements conducted at the same intervals of outpatient clinic visits. International Prostate Symptom Scores (IPSS) and uroflowmetry parameters, including maximum flow rate (Q_max) and voided volume (VV), were collected at each visit. At the time of study termination at 12 weeks, all patients completed a written survey using a 0-to-10-point scale for satisfaction. All enrolled participants completed the study protocol, and there were no missing data for any of the outcome variables over the 12-week follow-up period.

In-Office and Mobile App–Based Uroflowmetry

All patients uniformly underwent an in-office conventional uroflowmetry (CubeFlow_S; MCube Technology) at each visit before and after treatment according to the study schedule. Additional app-based uroflowmetry measurements were obtained using the sound-based mobile app proudP (Soundable Health, Inc), a Food and Drug Administration–listed class 2 uroflow meter that has been validated for flow prediction and VV measurements in previous studies [,]. The acoustic flow measurement system uses a wireless, smartphone-based approach with recording capabilities to analyze urinary flow. Acoustic data were captured in real time using a smartphone app. From the recorded sounds, parameters such as urinary volume, flow-related metrics (eg, peak urinary flow and average urinary flow), urinary flow patterns (eg, continuous or intermittent), and time-related parameters (eg, maximum voiding time and voiding duration) were calculated. Acoustic characteristics were assessed using audio processing, signal preprocessing, and spectral analysis techniques. A predictive model was then used to estimate urinary flow and associated parameters. Postprocessing produced data regarding accuracy and voiding metrics.

To minimize variability due to height or ambient surrounding noise, patients were instructed to ensure the restroom environment was as quiet as possible and place their smartphone approximately 80 cm away from the toilet before using the mobile app. They were then guided to launch the mobile uroflowmetry. While standing in front of the toilet, patients urinated, aiming for the center of the bowl, when possible, to optimize measurement accuracy. Before the actual experiment, all participants received standardized instructions on how to perform uroflowmetry using the in-hospital system to ensure consistent and reliable data collection.

The recorded sounds were analyzed using audio editing software (Audacity, version 2.2.2; Audacity Open Source Team and GoldWave). Signal preprocessing and postprocessing as well as the development of flow prediction models were conducted using MATLAB (version R2017b, 9.3.0; MathWorks, Inc) and Python (Python Software Foundation). To enhance accuracy, the acoustic analysis algorithm included preprocessing and postprocessing refinements to eliminate short-term artifacts and outliers, correct background noise levels, and remove specific noise bands. Validation of uroflowmetry parameters, including Q_max and VV, was performed in separate studies [].

Survey Method

To assess patient satisfaction with the mobile uroflowmetry system, we developed a brief, study-specific questionnaire tailored for use in this pilot validation study, and a single-session self-administered survey was conducted at the final in-office visit. The questionnaire was created collaboratively by the study investigators, including urologists and research coordinators, based on their clinical experience and anticipated domains of usability (eg, convenience, clarity of instructions, and perceived reliability of the mobile app). Patients were asked whether the use of the app-based monitoring (1) allowed better self-understanding of their clinical status, (2) improved the clinician’s understanding of their status, (3) was easy to use, and (4) was overall satisfactory. All scores were provided as a numerical value on a point scale ranging from 0 to 10.

Statistical Analysis

Independent 2-tailed t tests and equal-variance tests were used to assess whether there were statistically significant differences between conventional measurements obtained via in-office uroflowmetry–based and acoustic uroflowmetry–based mobile data collection. These tests were selected to validate the statistical characteristics of uroflowmetry measurements, including Q_max as the primary comparative factor, with VV and IPSS change as secondary measures. The analysis and calculations were conducted using Python (version 3.6.9; Python Software Foundation), along with the SciPy (version 1.5.14; Python Software Foundation) scientific computing package and R (version 4.3.1; R Foundation for Statistical Computing). Categorical variables were analyzed with chi-square and Fisher exact tests, and ANOVA was used for additional continuous variables. Normality was assessed using the Shapiro-Wilk test, and homogeneity of variance was evaluated with the Levene test. In cases where assumptions were not met, appropriate nonparametric alternatives (eg, Mann-Whitney U test and Kruskal-Wallis test) were used. Further assessment of the agreement of the 2 different uroflowmetry parameters was performed with Bland-Altman analysis, with the mean difference and 95% limits of agreement (defined as mean difference of +1.96 and –1.96 × SD of the paired differences) calculated according to standard procedures. To interpret clinical relevance, we adopted a provisional threshold of +10 mL/s and –10 mL/s for Q_max, as variations of this magnitude are generally regarded as unlikely to change the clinical interpretation of flow pattern or degree of obstruction in typical urodynamic practice [].

Results

A total of 46 treatment-naive patients with symptomatic BPH undergoing endoscopic surgery were screened, and 41 (89%) patients were finally enrolled, with 5 (11%) declining participation. The mean age of all patients was 67.4 (SD 5.5; range 58-79) years. Assessment of the accuracy and representability of acoustic uroflowmetry as compared to in-office measurements for Q_max resulted in a Pearson correlation of 0.743 (P<.001; ). The mean of the difference observed in the Bland-Altman analysis was 1.57 (SD 7.0), with upper and lower limits of agreement of 15.4 and –12.2, respectively.

Improvement in IPSS over the 12-week period was significant for all patients who underwent TURP for all specific parameters, including total IPSS and quality of life, as well as for both obstructive and irritative symptoms (; Figure S1 in ). Mean improvement of 10.2 and 8 points was observed for total IPSS and obstructive IPSS, respectively (both P<.001).

Specific uroflowmetry parameters, as measured from the mobile device, well reflected improvements in symptom scores, with baseline mean Q_max of 12.8 (SD 4.1) improving to 20.3 (SD 5.4) at the end of the study, similar to results measured from in-office uroflowmetry, which showed a similar range of improvement from a mean of 13.0 (SD 6.3) to 23.2 (SD 9.8) in the same period (). No significant differences in VV were observed.

Changes in IPSS to Q_max as measured by the app were significant overall, with modest correlation () and the highest Pearson correlation of –0.490 for obstructive IPSS, followed by –0.478 for total IPSS (Figure S2 in ). Individual questions for intermittency and weak stream showed the highest correlation as measured by acoustic uroflowmetry (r=–0.490 and r=–0.580, respectively).

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When stratified by prostate volume, patients with larger prostates (≥80 mL) demonstrated greater improvement in Q_max at 12 weeks compared to those with smaller prostates (<80 mL). Mean Q_max in the 80 mL or greater prostate volume group increased from 13.1 (SD 4.8) mL/s at baseline to 22.8 (SD 4.4) mL/s at 12 weeks using the app-based method compared with a 10.1 mL/s increase measured by in-office uroflowmetry. In contrast, patients with prostate volumes less than 80 mL showed a smaller mean increase (app: mean 13.0, SD 4.0 to mean 19.4, SD 5.3 mL/s; in office: mean 12.6, SD 5.3 to mean 22.8, SD 11.9 mL/s). Correlation between the 2 methods was also slightly higher in the larger prostate subgroup (r=0.692) than in the smaller prostate group (r=0.642; both P<.001), suggesting more consistent agreement in men with enlarged glands (Table S1 in ).

Further stratification by severity of IPSS showed both improvements reflected in patients with either moderate or severe IPSS, with a high Pearson correlation value of 0.751 and 0.734 in each group (all P<.001; Figure S3 in ).

Survey results for patient satisfaction are shown in . All patients were highly satisfied with the measurement process and felt the app was easy to use. Subjective assessment of the additive value of the app was remarkably high. No difference in the use of the app by men aged 70 years and older was observed.

A sample representation of post-TURP changes and measurements conducted with the mobile uroflowmetry is presented in , where a patient’s preoperative obstructive patterns and postoperative improvement of uroflowmetry plateau are well displayed, with an initial Q_max of 10.2 and VV of 297 improved to 20.0 and 324, respectively, at 12 weeks after surgery.

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Discussion

Principal Findings

This is the first prospective clinical trial to evaluate the effectiveness and feasibility of an acoustic app-based uroflowmetry to monitor patients after clinical intervention. The mobile measurements conducted at home were clinically reliable, with a strong correlation to IPSS improvement after surgery, also reliably reflecting the absolute improvement in Q_max with TURP, especially for patients with obstructive IPSS. Q_max, as measured with the app, showed consistent change regardless of prostate size as well as when stratified by severity of IPSS, suggesting that the technology can be reliably used in a wide spectrum of male patients with lower urinary tract symptoms. Older patients were equally satisfied with the process and felt at ease using the app, suggesting that as long as the patient is familiar with a mobile device, uroflowmetry measurements for clinical observation can be effectively conducted without technical difficulty.

Lee et al [] performed a prospective comparative analysis in 16 male pediatric patients using the same app to validate the technology’s strong correlation to standard measurements. A smartwatch-based uroflowmetry model was constructed by a Spanish team after extracting acoustic features from voiding stream sounds, similar to the method described in this study, and it displayed good correlation []. El Helou et al [] used a similar approach in 44 healthy young men, and by mapping total sound energy with VV, the model was successfully able to estimate flow rate with a mean absolute error of 2.41 mL/s. Dawidek et al [] compared a conventional uroflowmetry with an audio-based uroflowmetry (TeleSonoUroFlow) app, achieving a poor correlation for Q_max (r=0.12) and failing to show consistent results despite modest improvement in healthy individuals. However, these studies, by design, performed only comparisons of the mobile uroflowmetry versus conventional clinic-based measurements and did not evaluate whether the technology could accurately represent the changes that occur during and after treatment. Overall, a recent meta-analysis by Rangganata et al [] showed the efficacy of mobile acoustic uroflowmetry in male participants to be strong, with positive correlation for VV and Q_max, as shown in our study, as well as for other uroflowmetry parameters, including voiding time and average flow. Bladt et al [] also showed that at-home measurements can be as useful or even more representative of voiding patterns than hospital measurements, as shown in our sample patient (), in whom multiple app-based measurements were more informative and reliable in tracking voiding pattern changes.

The importance of uroflowmetry and changes in its parameters are paramount in assessing the success and efficacy of surgical treatment in BPH [-]. While preoperative Q_max values typically range from 6.18 to 8 mL/s, Q_max improves significantly after surgery, with studies reporting improvement up to 26.43 mL/s [,]. The average flow rate shows similar changes, with preoperative values from approximately 4.44 to 13.48 mL/s after TURP []. The patients in our cohort showed similar improvement, with most change found in large BPH. The significance of our study lies in the fact that mobile uroflowmetry was sufficient to measure the changes in such parameters after surgical intervention and reflect the measurements performed at outpatient visits, validating the efficacy for use in actual clinical practice. Unlike previous studies that primarily validated acoustic uroflowmetry in healthy volunteers or patients with stable lower urinary tract symptoms, this study evaluated its performance in a postoperative population undergoing active recovery after prostate surgery. In this context, uroflow parameters fluctuate considerably due to progressive relief of obstruction, healing of the bladder neck, and adaptation of detrusor contractility. Demonstrating consistent agreement between acoustic and conventional measurements across this dynamic postoperative trajectory supports the robustness of acoustic uroflowmetry beyond static or screening scenarios. Therefore, our findings extend the clinical applicability of this technology to longitudinal monitoring in the perioperative setting, where repeated, home-based assessments can provide meaningful insights into functional recovery. However, while tracking changes was significantly well correlated, our results suggest caution when considering complete replacement of conventional measurements, as the limits of agreement in this study (–12.2 to 15.4 mL/s) slightly exceed the prespecified reference range of +10 mL/s and –10 mL/s, suggesting that although the 2 devices show close overall agreement, individual measurements may differ modestly in real-world clinical use. This difference may reflect consistent measurement conditions at home despite pretraining and guidance during the trial or may result from intraindividual variability in uroflowmetry, which by itself is known to reach approximately 10 mL/s []. This finding highlights the need and necessity for multiple measurements in a single individual during clinical evaluation and monitoring, underscoring the importance and potential for remote mobile measurements.

Another interesting point to mention was that a stronger agreement was observed in men with larger prostates or higher baseline IPSS. This may reflect the more stable and reproducible flow characteristics typically seen in obstructive voiding patterns, in which urinary flow is typically slower and of longer duration, producing clearer and less noisy acoustic signals that enhance the reliability of the app’s waveform detection. Conversely, individuals with smaller prostates or milder symptoms often exhibit higher peak variability and shorter flow times, which can amplify measurement discrepancies between acoustic and conventional methods. These subgroup findings suggest that app-based uroflowmetry may be particularly accurate for monitoring patients with clinically significant bladder outlet obstruction, while careful interpretation is still warranted in those with near-normal flow profiles.

Beyond demonstrating technical validity, this study also highlights the digital health potential of acoustic uroflowmetry. The app-based measurement system allows patients to record voiding data conveniently without additional equipment, such as measuring cups or paper logs. Previous studies have reported higher satisfaction and adherence with app-based systems compared to conventional uroflowmetry, even among older adults unfamiliar with smartphones []. In our cohort, similar usability was observed among participants aged more than 70 years, likely reflecting both the intuitive interface design and the brief in-office education that enhanced confidence and accuracy of use [,]. The automatic generation of an electronic voiding diary may have further increased engagement by reducing manual documentation and simplifying self-tracking.

From a clinical workflow perspective, such usability supports integration of acoustic uroflowmetry into telemedicine and self-management pathways for BPH and postoperative monitoring. Home-based acoustic measurements can be securely transmitted to clinicians for asynchronous review, enabling continuous monitoring of recovery trends and early identification of voiding deterioration without frequent in-person visits. When combined with patient-reported outcomes, such as IPSS, app-based flow metrics may enhance remote clinical decision-making and support personalized treatment adjustments. Integration with electronic medical records and automated alerts based on individualized thresholds could further streamline care within digital urology ecosystems.

Nevertheless, several equity and accessibility considerations should be acknowledged. Smartphone literacy remains a potential barrier, particularly among older or socioeconomically disadvantaged populations. In our study, targeted education and a simplified user interface mitigated many of these challenges, but broader implementation will require interfaces accommodating sensory or cognitive limitations. Cost and device availability also remain relevant, as not all patients may have access to compatible smartphones or stable internet connections, potentially widening digital health disparities. Data privacy and cybersecurity represent additional priorities. Because acoustic recordings and voiding profiles constitute personal health information, strict adherence to encryption, anonymization, and data protection regulations (eg, General Data Protection Regulation and Health Insurance Portability and Accountability Act) is essential. Finally, regulatory approval processes for software as a medical device must be clearly defined to ensure safety, performance, and clinical accountability. Early engagement with regulatory authorities and compliance with international validation frameworks will be crucial for widespread clinical adoption.

Collectively, these findings suggest that app-based uroflowmetry not only provides accurate and reproducible measurements but also aligns with the evolving paradigm of patient-centered, connected urological care. With careful attention to usability, privacy, and equity, such technologies could substantially enhance accessibility to postoperative monitoring and chronic symptom management through scalable digital health integration.

Limitations

This study is not without limitations. First, conventional uroflowmetry was performed at a single session, with repeat measurements conducted only if the patient was unable to void at the first trial or had low VV (≤150 mL), to ensure reliability of the uroflowmetry measurements. However, a single in-office voiding trial may overestimate or underestimate the actual symptoms and change over clinical course, and repeated measurements, as in the mobile uroflowmetry, may be required. Second, no information on TURP clinical variables, such as baseline prostate-specific antigen, resection volume, or pathology, was included in our analysis. While the loss of such information was detrimental to our results, resection percentage as a surrogate for completeness of adenoma resection may have shown a strong correlation with IPSS and Q_max change as estimated from both app-based and in-office measurements, supporting our findings. Moreover, as this study aimed to validate agreement between mobile and in-office uroflowmetry, any such factors would likely have affected both modalities equally and thus are unlikely to alter the comparative findings. Given the exploratory nature of this study, survey questionnaires were custom made and were not validated in a separate study, which may undermine the reliability of the reported outcomes. Limitations associated with the lack of psychometric validation are acknowledged, and future studies should incorporate standardized and validated patient-reported outcome measures to strengthen the assessment of usability. Finally, this study did not include a randomized control group and was conducted in a pilot prospective observational trial setting, limiting the strength of causal inference. However, this design was deliberately chosen to evaluate the feasibility and assess the preliminary performance of the mobile uroflowmetry in a clinical treatment scenario, with the plan of performing larger studies in a randomized and controlled framework to establish the potential replacement of conventional methods. The limited sample size and observational design of this study suggest potential for mobile uroflowmetry; however, they are insufficient to fully support complete replacement of conventional uroflowmetry. Future head-to-head randomized controlled trials comparing long-term outcomes between mobile uroflowmetry and in-office measurements are required to further validate the clinical efficacy of mobile methods. In particular, appropriate methods, such as Bonferroni or false discovery rate correction to address multiple testing and repeated measures, will be required.

Taken together, this study demonstrates the feasibility of using mobile, app-based uroflowmetry as a reliable alternative to conventional in-office measurements. By overcoming the spatial and temporal limitations inherent to traditional uroflowmetry, app-based measurements enable continuous, home-based assessment of postoperative urinary flow dynamics. Unlike previous validation studies limited to healthy or stable populations, our findings extend the applicability of this technology to a postsurgical cohort, showing its potential to capture dynamic recovery patterns and detect functional improvement without requiring frequent outpatient visits. Although no cases of acute retention or early stricture occurred during follow-up, the ability to remotely monitor flow changes suggests a role for early detection of postoperative complications and personalized recovery tracking.

Nonetheless, these findings should be interpreted within the scope of a feasibility study. This work establishes proof of concept and short-term clinical reliability but does not yet address long-term adherence, scalability, or outcome-driven end points. The logical next steps include conducting a randomized controlled trial comparing acoustic and conventional uroflowmetry in diverse clinical settings, followed by broader real-world implementation studies to evaluate cost-effectiveness, user engagement, and system integration within telehealth and electronic medical record platforms. With such validation, app-based uroflowmetry could evolve into a scalable, patient-centered component of precision urological care.

Conclusions

In this prospective pilot observational study, app-based uroflowmetry (proudP) measurements showed reasonable concordance with conventional in-office testing, indicating its feasibility as a tool for perioperative surveillance in BPH surgery. The app-based system effectively reflected both in-office flow values and longitudinal changes in symptom severity, as measured by IPSS, suggesting potential for reliable home-based monitoring of postoperative recovery. Nonetheless, the absence of randomization, the use of a single cohort, and the limited follow-up necessitate caution in interpreting these findings. Future large-scale randomized and real-world implementation studies across diverse populations with lower urinary tract symptoms are warranted to establish the clinical validity, cost-effectiveness, and scalability of app-based uroflowmetry as a practical extension of telemedicine in contemporary urological care.

• SoFI’s stock is down Friday afternoon after announcing a share sale.

• The company’s stock is still riding high after beating analysts earnings expectations for seven of the last eight quarters.

SoFi’s been on a winning streak. Today’s it’s taking a breather.

Shares of SoFi Technologies (SOFI), a fintech-turned-bank, are taking a hit after a $1.5 billion share sale announcement on Thursday caught investors and analysts off guard. The most recent capital raise was its second in six months. Still, its stock remains aloft, nearly doubling so far this year.

The company has been riding high on its achievements over the last two years, with seven of its last eight quarterly reports beating analyst earnings expectations, according to Visible Alpha. And the company plans to invest in its existing businesses, relaunch crypto trading, and expand its product offerings—good reasons to have cash on hand.

SoFi plans to expand its offerings in the coming year. That could boost its stck, but the shares have rallied for much of 2025, sitting near all-time highs, and have recently slid after the company announced a share sale to raise capital. Some analysts believe the fintech could qualify for S&P 500 membership, which could also give the shares a lift.

SoFi’s second capital raise in two quarters wasn’t widely expected. Keefe, Bruyette & Woods analyst Tim Switzer said in a recent note that he was “a little surprised” since the latest share sale followed another of the same amount in July. That said, the firm’s capital levels are low compared to bank peers, according to Switzer.

“We believe the raise is largely opportunistic given the stock is near all-time highs,” Switzer wrote yesterday.

The company priced the shares at $27.50 apiece, just a touch below their November all-time high of around $32. Visible Alpha’s Street consensus target is just under $26, perhaps an indication that the company managed a good price—though the Street’s outlook on the shares, based on ratings, is broadly neutral. The shares closed 6% lower, at $27.78.

SoFi started out as a fintech company primarily offering student loan refinancing, but has since transformed into a full-service bank offering bank accounts, personal loans and investment products. It also relaunched a crypto trading platform last month after pausing that service in 2023 as it was securing its national bank charter. SoFi plans to launch its own branded stablecoin next year, according to the company.

KARACHI  –  The State Bank of Pakistan (SBP) injected Rs2,610.8 billion through Reverse Repo Purchase and Shariah Compliant Mudarabah based Open Market Operations (OMO) on Friday to maintain liquidity in the market.  The central bank conducted the Open Market Operation, Reverse Repo Purchase (Injection) for 7 and 14-day tenors on December 05, 2025, and injected Rs2,437.8 billion against 14 bids while other Rs173 billion were inserted through Shariah Compliant Mudarabah based OMO. The central bank received 11 bids for the 14-day Reverse Repo Purchase, cumulatively offering Rs2,384.8 billion at the rate of return ranging between 11.01 to 11.08 percent.

The SBP accepted all the bids with the entire amount at 11.01 percent rate of return. 

Moreover, the SBP also received 3 quotes for the 7-day tenor, cumulatively offering Rs53 billion at the rate of return ranging between 11.03 percent to 11.05 percent. 

The SBP accepted the entire amount at 11.03 percent rate of return. 

Meanwhile, SBP also conducted Shariah Compliant Mudarabah based Open Market Operation for the 7 and 14-day tenors. The central bank did not receive any bid for the 14-day tenor while 3 quotes were received for 7-day tenor offering Rs218 billion at rate of return ranging between 11.01 to 11.06 percent. SBP accepted Rs173 billion against two bids at 11.05 percent rate of return.

TikTok and other social media platforms are hosting AI-generated deepfake videos of doctors whose words have been manipulated to help sell supplements and spread health misinformation.

The factchecking organisation Full Fact has uncovered hundreds of such videos featuring impersonated versions of doctors and influencers directing viewers to Wellness Nest, a US-based supplements firm.

All the deepfakes involve real footage of a health expert taken from the internet. However, the pictures and audio have been reworked so that the speakers are encouraging women going through menopause to buy products such as probiotics and Himalayan shilajit from the company’s website.

The revelations have prompted calls for social media giants to be much more careful about hosting AI-generated content and quicker to remove content that distorts prominent people’s views.

“This is certainly a sinister and worrying new tactic,” said Leo Benedictus, the factchecker who undertook the investigation, which Full Fact published on Friday.

He added that the creators of deepfake health videos deploy AI so that “someone well-respected or with a big audience appears to be endorsing these supplements to treat a range of ailments”.

Prof David Taylor-Robinson, an expert in health inequalities at Liverpool University, is among those whose image has been manipulated. In August, he was shocked to find that TikTok was hosting 14 doctored videos purporting to show him recommending products with unproven benefits.

Though Taylor-Robinson is a specialist in children’s health, in one video the cloned version of him was talking about an alleged menopause side-effect called “thermometer leg”.

The fake Taylor-Robinson recommended that women in menopause should visit a website called Wellness Nest and buy what it called a natural probiotic featuring “10 science-backed plant extracts, including turmeric, black cohosh, Dim [diindolylmethane] and moringa, specifically chosen to tackle menopausal symptoms”.

Female colleagues “often report deeper sleep, fewer hot flushes and brighter mornings within weeks”, the deepfake doctor added.

The real Taylor-Robinson discovered that his likeness was being used only when a colleague alerted him. “It was really confusing to begin with – all quite surreal,” he said. “My kids thought it was hilarious.

“I didn’t feel desperately violated, but I did become more and more irritated at the idea of people selling products off the back of my work and the health misinformation involved.”

The footage of Taylor-Robinson used to make the deepfake videos came from a talk on vaccination he gave at a Public Health England (PHE) conference in 2017 and a parliamentary hearing on child poverty at which he gave evidence in May this year. In one misleading video, he was depicted swearing and making misogynistic comments while discussing menopause.

TikTok took down the videos six weeks after Taylor-Robinson complained. “Initially, they said some of the videos violated their guidelines but some were fine. That was absurd – and weird – because I was in all of them and they were all deepfakes. It was a faff to get them taken down,” he said.

Full Fact found that TikTok was also carrying eight deepfakes featuring doctored statements by Duncan Selbie, the former chief executive of PHE. Like Taylor-Robinson, he was falsely shown talking about menopause, using video taken from the same 2017 event where Taylor-Robinson spoke.

One, also about “thermometer leg”, was “an amazing imitation”, Selbie said. “It’s a complete fake from beginning to end. It wasn’t funny in the sense that people pay attention to these things.”

Full Fact also found similar deepfakes on X, Facebook and YouTube, all linked to Wellness Nest or a linked British outlet called Wellness Nest UK. It has posted apparent deepfakes of high-profile doctors such as Prof Tim Spector and another diet expert, the late Dr Michael Mosley.

Wellness Nest told Full Fact that deepfake videos encouraging people to visit the firm’s website were “100% unaffiliated” with its business. It said it had “never used AI-generated content”, but “cannot control or monitor affiliates around the world”.

Helen Morgan, the Liberal Democrat health spokesperson, said: “From fake doctors to bots that encourage suicide, AI is being used to prey on innocent people and exploit the widening cracks in our health system.

“Liberal Democrats are calling for AI deepfakes posing as medical professionals to be stamped out, with clinically approved tools strongly promoted so we can fill the vacuum.

“If these were individuals fraudulently pretending to be doctors they would face criminal prosecution. Why is the digital equivalent being tolerated?

“Where someone seeks health advice from an AI bot they should be automatically referred to NHS support so they can get the diagnosis and treatment they actually need, with criminal liability for those profiting from medical disinformation.”

A TikTok spokesperson said: “We have removed this content [relating to Taylor-Robinson and Selbie] for breaking our rules against harmful misinformation and behaviours that seek to mislead our community, such as impersonation.

“Harmfully misleading AI-generated content is an industry-wide challenge, and we continue to invest in new ways to detect and remove content that violates our community guidelines.”

The Department of Health and Social Care was approached for comment.