Background: The proliferation of internet-based health information has intensified cyberchondria, or anxiety resulting from excessive health-related searches. The relationship between cyberchondria and suicidal ideation remains underexplored, although there are indications that people with high levels of cyberchondria may also be suicidal. Understanding this relationship is critical, given rising digital health-seeking behaviors and the need to mitigate suicide risk. Emerging evidence suggests that psychological distress can mediate the relationship between cyberchondria and suicidal ideation. However, to the best of our knowledge, no research has directly examined these associations. Objective: This study had two aims. The first was to examine the relationship between cyberchondria and suicidal ideation in a sample of the general Chinese population. The second aim was to investigate the possible role of psychological distress, reflecting the symptoms of depression and anxiety, as a mediator in the relationship between cyberchondria and suicidal ideation. Methods: Data were obtained from a cross-sectional and web-based survey conducted in 2024. Structural equation modeling analysis was used to assess the hypothesized association between cyberchondria and suicidal ideation, as well as the mediating effect of psychological distress on this association. The Cyberchondria Severity Scale-12 items, Suicidal Ideation Attributes Scale, and Kessler Psychological Distress Scale-10 items were used to measure cyberchondria, suicidal ideation, and psychological distress, respectively. Standardized (β) estimates, along with their 95% CIs, were calculated for all structural paths, adjusting for participants’ background characteristics. Results: A total of 2415 individuals completed the questionnaire (response rate=98.5%). Scores on the Cyberchondria Severity Scale-12 items ranged from 12 to 60, with the mean score being 40 (SD 7.9). The mean score on the Suicidal Ideation Attributes Scale was 12.7 (SD 9.9). Scores on the Kessler Psychological Distress Scale-10 items ranged from 10 to 50, and the mean score was 22 (SD 6.9). Cyberchondria, suicidal ideation, and psychological distress were significantly correlated. Structural equation modeling revealed a significant association between cyberchondria and psychological distress (β=.281; P<.001), between psychological distress and suicidal ideation (β=.504; P<.001), and between cyberchondria and suicidal ideation (β=.107; P<.001). The indirect effect of cyberchondria on suicidal ideation through psychological distress was also significant (β=.142; P<.001). Conclusions: The main contribution of this study is that it highlights an important relationship between cyberchondria and suicidal ideation, with a direct and statistically significant association between these variables. Their relationship is also mediated by psychological distress, which reflects the role of depressive and anxiety symptoms.

Background: The last decade has witnessed major advances in the development of artificial intelligence (AI) technologies for use in health care. One of the most promising areas of research that has potential clinical utility is the use of AI in pathology to aid cancer diagnosis and management. While the value of using AI to improve the efficiency and accuracy of diagnosis cannot be underestimated, there are challenges in the development and implementation of such technologies. Notably, questions remain about public support for the use of AI to assist in pathological diagnosis and for the use of health care data, including data obtained from tissue samples, to train algorithms. Objective: This study aimed to investigate public awareness of and attitudes toward AI in pathology research and practice. Methods: A nationally representative, cross-sectional, web-based mixed methods survey (N=1518) was conducted to assess the UK public’s awareness of and views on the use of AI in pathology research and practice. Respondents were recruited via Prolific, an online research platform. To be eligible for the study, participants had to be aged >18 years, be UK residents, and have the capacity to express their own opinion. Respondents answered 30 closed-ended questions and 2 open-ended questions. Sociodemographic information and previous experience with cancer were collected. Descriptive and inferential statistics were used to analyze quantitative data; qualitative data were analyzed thematically. Results: Awareness was low, with only 23.19% (352/1518) of the respondents somewhat or moderately aware of AI being developed for use in pathology. Most did not support a diagnosis of cancer (908/1518, 59.82%) or a diagnosis based on biomarkers (694/1518, 45.72%) being made using AI only. However, most (1478/1518, 97.36%) supported diagnoses made by pathologists with AI assistance. The adjusted odds ratio (aOR) for supporting AI in cancer diagnosis and management was higher for men (aOR 1.34, 95% CI 1.02-1.75). Greater awareness (aOR 1.25, 95% CI 1.10-1.42), greater trust in data security and privacy protocols (aOR 1.04, 95% CI 1.01-1.07), and more positive beliefs (aOR 1.27, 95% CI 1.20-1.36) also increased support, whereas identifying more risks reduced the likelihood of support (aOR 0.80, 95% CI 0.73-0.89). In total, 3 main themes emerged from the qualitative data: bringing the public along, the human in the loop, and more hard evidence needed, indicating conditional support for AI in pathology with human decision-making oversight, robust measures for data handling and protection, and evidence for AI benefit and effectiveness. Conclusions: Awareness of AI’s potential use in pathology was low, but attitudes were positive, with high but conditional support. Challenges remain, particularly among women, regarding AI use in cancer diagnosis and management. Apprehension persists about the access to and use of health care data by private organizations.

Background: Delirium in intensive care unit (ICU) patients poses a significant challenge, affecting patient outcomes and health care efficiency. Developing an accurate, real-time prediction model for delirium represents an advancement in critical care, addressing needs for timely intervention and resource optimization in ICUs. Objective: We aimed to create a novel machine learning model for delirium prediction in ICU patients using only continuous physiological data. Methods: We developed models integrating routinely available clinical data, such as age, sex, and patient monitoring device outputs, to ensure practicality and adaptability in diverse clinical settings. To confirm the reliability of delirium determination records, we prospectively collected results of Confusion Assessment Method for the ICU (CAM-ICU) evaluations performed by qualified investigators from May 17, 2021, to December 23, 2022, determining Cohen κ coefficients. Participants were included in the study if they were aged ≥18 years at ICU admission, had delirium evaluations using the CAM-ICU, and had data collected for at least 4 hours before delirium diagnosis or nondiagnosis. The development cohort from Yongin Severance Hospital (March 1, 2020, to January 12, 2022) comprised 5478 records: 5129 (93.62%) records from 651 patients for training and 349 (6.37%) records from 163 patients for internal validation. For temporal validation, we used 4438 records from the same hospital (January 28, 2022, to December 31, 2022) to reflect potential seasonal variations. External validation was performed using data from 670 patients at Ajou University Hospital (March 2022 to September 2022). We evaluated machine learning algorithms (random forest [RF], extra-trees classifier, and light gradient boosting machine) and selected the RF model as the final model based on its performance. To confirm clinical utility, a decision curve analysis and temporal pattern for model prediction during the ICU stay were performed. Results: The κ coefficient between labels generated by ICU nurses and prospectively verified by qualified researchers was 0.81, indicating reliable CAM-ICU results. Our final model showed robust performance in internal validation (area under the receiver operating characteristic curve [AUROC]: 0.82; area under the precision-recall curve [AUPRC]: 0.62) and maintained its accuracy in temporal validation (AUROC: 0.73; AUPRC: 0.85). External validation supported its effectiveness (AUROC: 0.84; AUPRC: 0.77). Decision curve analysis showed a positive net benefit at all thresholds, and the temporal pattern analysis showed a gradual increase in the model scores as the actual delirium diagnosis time approached. Conclusions: We developed a machine learning model for delirium prediction in ICU patients using routinely measured variables, including physiological waveforms. Our study demonstrates the potential of the RF model in predicting delirium, with consistent performance across various validation scenarios. The model uses noninvasive variables, making it applicable to a wide range of ICU patients, with minimal additional risk.

Background: Men who have sex with men have a disproportionately high prevalence of HIV worldwide. In Brazil, men who have sex with men account for over 15% of HIV cases, substantially higher than the general population prevalence of 0.6%. Pre-exposure prophylaxis (PrEP) is a critical biomedical strategy for reducing HIV transmission, yet adherence remains challenging due to stigma, logistical barriers, and the need for regular clinical follow-ups. TelePrEP, a telehealth-based approach to PrEP follow-up, has emerged as a potential solution to improve accessibility and reduce stigma. However, the perspectives of users and health care providers on this intervention remain understudied in low- and middle-income countries, such as Brazil. Objective: This study aims to examine the experiences and perceptions of users and health care professionals regarding TelePrEP, an asynchronous remote consultation model, in 5 PrEP services across 3 Brazilian regions (southeast, south, and northeast). Methods: We conducted 19 in-depth interviews with PrEP users (aged between 23 and 58 years) and 6 interviews with health care professionals (aged between 35 and 61 years). Users were recruited from 5 public health care services, including outpatient HIV clinics and testing centers. The interviews explored motivations for PrEP use, experiences with in-person and remote consultations, perceived advantages and disadvantages of TelePrEP, and overall satisfaction. Thematic analysis was conducted using NVivo software. Results: Users reported greater convenience, increased autonomy, and reduced stigma, highlighting that the remote consultations eliminated the discomfort of discussing personal topics in person and minimized the need for frequent visits to health care facilities. Many felt that TelePrEP simplified HIV prevention, normalized PrEP use, and contributed to more sustainable adherence while also expressing confidence that periodic laboratory testing was sufficient for monitoring their health. Conversely, health care professionals raised concerns about the loss of personal connection with users, which they perceived as essential for detecting health issues and ensuring PrEP adherence. They also noted that TelePrEP could hinder the identification of sexually transmitted infections due to the absence of direct clinical assessments, and some questioned whether TelePrEP compromised the quality of care, fearing that users might delay reporting symptoms or other health concerns. Conclusions: To effectively address the needs of both groups, the successful implementation of telehealth PrEP services must consider these differing perceptions. Further research is essential to explore implementation in diverse settings and enhance the training of health care professionals to address the specific requirements of PrEP care.

Background: Symptom checker apps (SCAs) are layperson-facing tools that advise on whether and where to seek care, or possible diagnoses. Previous research has primarily focused on evaluating the accuracy, safety, and usability of their recommendations. However, studies examining SCAs’ impact on clinical care, including the patient-physician interaction and satisfaction with care, remain scarce. Objective: This study aims to evaluate the effects of an SCA on satisfaction with the patient-physician interaction in acute care settings. Additionally, we examined its influence on patients’ anxiety and trust in the treating physician. Methods: This parallel-group, randomized controlled trial was conducted at 2 emergency departments of an academic medical center and an emergency practice in Berlin, Germany. Low-acuity patients seeking care at these sites were randomly assigned to either self-assess their health complaints using a widely available commercial SCA (Ada Health) before their first encounter with the treating physician or receive usual care. The primary endpoint was patients’ satisfaction with the patient-physician interaction, measured by the Patient Satisfaction Questionnaire (PSQ). The secondary outcomes were patients’ satisfaction with care, their anxiety levels, and physicians’ satisfaction with the patient-physician interaction. We used linear mixed models to assess the statistical significance of primary and secondary outcomes. Exploratory descriptive analyses examined patients’ and physicians’ perceptions of the SCA’s utility and the frequency of patients questioning their physician’s authority. Results: Between April 11, 2022, and January 25, 2023, we approached 665 patients. A total of 363 patients were included in the intention-to-treat analysis of the primary outcome (intervention: n=173, control: n=190). PSQ scores in the intervention group were similar to those in the control group (mean 78.5, SD 20.0 vs mean 80.8, SD 19.6; estimated difference –2.4, 95% CI –6.3 to 1.1, P=.24). Secondary outcomes, including patients’ and physicians’ satisfaction with care and patient anxiety, showed no significant group differences (all P>.05). Patients in the intervention group were more likely to report that the SCA had a beneficial (66/164, 40.2%) rather than a detrimental (3/164, 1.8%) impact on the patient-physician interaction, with most reporting no effect (95/164, 57.9%). Similar patterns were observed regarding the SCA’s perceived effect on care. In both groups, physicians rarely reported that their authority had been questioned by a patient (intervention: 2/188, 1.1%; control: 4/184, 2.2%). While physicians more often found the SCA helpful rather than unhelpful, the majority indicated it was neither helpful nor unhelpful for the encounter. Conclusions: We found no evidence that the SCA improved satisfaction with the patient-physician interaction or care in an acute care setting. By contrast, both patients and their treating physicians predominantly described the SCA’s impact as beneficial. Our study did not identify negative effects of SCA use commonly reported in the literature, such as increased anxiety or diminished trust in health care professionals. Trial Registration: German Clinical Trial Register DRKS00028598; https://drks.de/search/en/trial/DRKS00028598/entails

Background: As the percentage of the older population increases, it is accompanied by an increase in the prevalence of Parkinson disease (PD). People with PD experience a range of nonmotor symptoms, including pain, constipation, dysphagia, sleep disturbances, and fatigue. Improving self-care is necessary for people with PD because it is a chronic disease that requires lifelong management. In our previous study, we developed a mobile app (Yon PD app) to monitor nonmotor symptoms of PD. In this study, we investigated the long-term effects of the app in a larger group of people. Objective: This study aimed to examine the effectiveness of a mobile app on the management of self-care in people with PD. Methods: This was a randomized controlled trial. People with PD aged ≥50 years and able to use a smartphone were recruited from the neurology outpatient clinic of a tertiary hospital in South Korea. In total, 102 participants were enrolled in this study. The intervention group was requested to record 5 nonmotor symptoms (pain, constipation, dysphagia, sleep disturbances, and fatigue) for 12 weeks using the mobile app. The control group was requested to record these 5 nonmotor symptoms on a paper questionnaire. General characteristics including age, sex, level of education, disease severity, and comorbidities were examined at baseline. The degree of self-care was examined using the Self-Care of Chronic Illness Inventory at baseline, 6 weeks, and 12 weeks. At 12 weeks, satisfaction with the app was also examined. General characteristics and satisfaction with the app were analyzed using descriptive statistics. The effect of the app on self-care was analyzed using the repeated-measures ANOVA with an α level of .05. Results: In total, 93 participants were included in the analysis. There were 41 and 52 participants in the intervention and control groups, respectively. The general characteristics of the 2 groups were comparable. Monitoring nonmotor symptoms with the app effectively increased self-care maintenance (F₂₁₈₂=4.087; P=.02) and prevented a decrease in self-care monitoring (F₂₁₈₂=3.155; P=.045). However, using the app was ineffective in improving self-care management (F₂₁₈₂=1.348; P=.26). Self-care management gradually decreased over the 12-week period in both groups. The intervention (n=41) adherence rate reached 60.84% at 6 weeks but decreased to 41.87% by 12 weeks. Conclusions: Participants were able to improve the degree of self-care by monitoring their nonmotor symptoms using the app. However, additional strategies that increase motivation and enjoyment are required to improve adherence. Trial Registration: Clinical Research Information Service KCT0006433; https://tinyurl.com/3vmf435m

Background: Panic disorder is an anxiety disorder marked by severe fear of panic attacks in the absence of causes. Agoraphobia is a related anxiety disorder, which involves fear and avoidance of specific situations in which escape or help may be difficult. Both can be debilitating and impair well-being. One treatment option may be internet-based cognitive behavioral therapy (iCBT), which allows large-scale application and may overcome treatment barriers for some individuals. Objective: This study aimed to evaluated the effectiveness of a novel online self-help intervention for panic disorder with or without agoraphobia. As our primary hypotheses, we expected the intervention to improve panic and agoraphobia symptoms and well-being. Our secondary hypotheses entailed improvements in daily functioning, mental health literacy, working ability, and health care use in the intervention group. Methods: German-speaking patients (N=156) aged 18-65 years with internet access and a diagnosis of panic disorder with or without agoraphobia were recruited for this randomized controlled trial. The intervention group (n=82) received access to a 12-week online self-help program entailing psychoeducation, cognitive restructuring, exposure, and mindfulness elements. The control group (n=72) received care as usual during the study period and was offered the prospect of using the program after 12 weeks. The primary outcomes were assessed via the Panic and Agoraphobia Scale (PAS) and the WHO (World Health Organization)-5 Well-Being Index (WHO-5). Mixed effect models were computed using multivariate imputation by chained equation for the analysis of intervention effects. Results: In the intervention group, participants completed on average 7.3 out of 12 (60.8%) modules, and 27 out of 82 (32.1%) participants finished the whole course. Changes in PAS revealed a significant effect in favor of the intervention group (t_110.1=–2.22, P_adj=.03) with a small to moderate effect size (d=–0.37, 95% CI –0.70 to –0.04). No significant effect was found for the second primary outcome WHO-5 (t_149.8=1.35, P_adj=.09) or the secondary outcomes. Improvements were observed in anxiety (t_206.8=–4.12; P<.001; Cohen d=–0.60, 95% CI –0.089 to –0.32) and depression (t_257.4=–3.20; P<.001; Cohen d=–0.41 95% CI –0.66 to –0.16). No negative effects were associated with the intervention (t₁₂₅=–1.14, P=.26). Conclusions: The presented online intervention can help reduce the core symptomatology of panic disorder and agoraphobia, as well as anxiety symptoms and associated depression. No effects were found for well-being and secondary outcomes. This may be due to higher illness burden in the intervention group and possibly the COVID pandemic, which caused unique challenges to patients suffering from panic disorder. Therefore, further research and intervention adaptations may be warranted to improve these outcomes. Trial Registration: German Clinical Trials Register DRKS00023800; https://drks.de/search/en/trial/DRKS00023800

Background: Large language model (LLM) artificial intelligence (AI) tools have the potential to streamline health care administration by enhancing efficiency in document drafting, resource allocation, and communication tasks. Despite this potential, the adoption of such tools among hospital administrators remains understudied, particularly at the individual level. Objective: This study aims to explore factors influencing the adoption and use of LLM AI tools among hospital administrators in China, focusing on enablers, barriers, and practical applications in daily administrative tasks. Methods: A multicenter, cross-sectional, descriptive qualitative design was used. Data were collected through semistructured face-to-face interviews with 31 hospital administrators across 3 tertiary hospitals in Beijing, Shenzhen, and Chengdu from June 2024 to August 2024. The Colaizzi method was used for thematic analysis to identify patterns in participants’ experiences and perspectives. Results: Adoption of LLM AI tools was generally low, with significant site-specific variations. Participants with higher technological familiarity and positive early experiences reported more frequent use, while barriers such as mistrust in tool accuracy, limited prompting skills, and insufficient training hindered broader adoption. Tools were primarily used for document drafting, with limited exploration of advanced functionalities. Participants strongly emphasized the need for structured training programs and institutional support to enhance usability and confidence. Conclusions: Familiarity with technology, positive early experiences, and openness to innovation may facilitate adoption, while barriers such as limited knowledge, mistrust in tool accuracy, and insufficient prompting skills can hinder broader use. LLM AI tools are now primarily used for basic tasks such as document drafting, with limited application to more advanced functionalities due to a lack of training and confidence. Structured tutorials and institutional support are needed to enhance usability and integration. Targeted training programs, combined with organizational strategies to build trust and improve accessibility, could enhance adoption rates and broaden tool use. Future quantitative investigations should validate the adoption rate and influencing factors.

Background: As the number of people diagnosed with cancer continues to increase, self-management has become crucial for patients recovering from cancer surgery or undergoing chemotherapy. Technology has emerged as a key tool in supporting self-management, particularly through interventions that promote physical activity, which is important for improving health outcomes and quality of life for patients with cancer. Despite the growing availability of digital tools that facilitate physical activity tracking, high-level evidence of their long-term effectiveness remains limited. Objective: This study aimed to investigate the effect of long-term physical activity on patients with cancer by categorizing them into active and inactive groups based on step count time-series data using the mobile health intervention, the Walkon app (Swallaby Co, Ltd.). Methods: Patients with cancer who had previously used the Walkon app in a previous randomized controlled trial were chosen for this study. Walking step count data were acquired from the app users. Biometric measurements, including BMI, waist circumference, blood sugar levels, and body composition, along with quality of life (QOL) questionnaire responses (European Quality of Life 5 Dimensions 5 Level version and Health-related Quality of Life Instrument with 8 Items), were collected during both the baseline and 6-month follow-up at an outpatient clinic. To analyze step count patterns over time, the concept of sample entropy was used for patient clustering, distinguishing between the active walking group (AWG) and the inactive walking group (IWG). Statistical analysis was performed using the Shapiro-Wilk test for normality, with paired t tests for parametric data, Wilcoxon signed-rank tests for nonparametric data, and chi-square tests for categorical variables. Results: The proposed method effectively categorized the AWG (n=137) and IWG (n=75) based on step count trends, revealing significant differences in daily (4223 vs 5355), weekly (13,887 vs 40,247), and monthly (60,178 vs 174,405) step counts. Higher physical activity levels were observed in patients with breast cancer and younger individuals. In terms of biometric measurements, only waist circumference (P=.01) and visceral fat (P=.002) demonstrated a significant improvement exclusively within the AWG. Regarding QOL measurements, aspects such as energy (P=.01), work (P<.003), depression (P=.02), memory (P=.01), and happiness (P=.05) displayed significant improvements solely in the AWG. Conclusions: This study introduces a novel methodology for categorizing patients with cancer based on physical activity using step count data. Although significant improvements were noted in the AWG, particularly in QOL and specific physical metrics, differences in 6-month change between the AWG and IWG were statistically insignificant. These findings highlight the potential of digital interventions in improving outcomes for patients with cancer, contributing valuable insights into cancer care and self-management. Trial Registration: Clinical Research Information Service by Korea Centers for Diseases Control and Prevention, Republic of Korea KCT0005447; https://tinyurl.com/3zc7zvzz