Background: The global aging population faces great challenges. Wearable activity trackers have emerged as tools to promote physical activity among older adults, potentially improving health outcomes. However, the effectiveness of such interventions on physical activity, body composition, and physical function among community-dwelling older adults remains debated. Objective: This study conducted a systematic review and meta-analysis to evaluate the impact of wearable activity tracker–based interventions on physical activity, body composition, and physical function among community-dwelling older adults. Methods: We searched the PubMed, Embase, Web of Science, and CENTRAL databases from inception until January 2025 to identify related randomized controlled trials. The outcomes were focused on physical activity (physical activity time, daily step count, and daily sedentary time); body composition (BMI and body fat); and physical function (timed up and go test and chair stand test). Subgroup analysis by different controls (usual care or conventional interventions) and different follow-ups (immediate or short term) were performed. Results: In total 23 trials with 4566 participants were eligible for analysis. Compared to usual care, there was lo- to moderate-certainty evidence that the wearable activity tracker–based interventions significantly increased physical activity time (standardized mean difference [SMD]=0.28, 95% CI 0.10-0.47; P=.003) and daily step counts (SMD=0.58, 95% CI 0.33-0.83; P<.001) immediately after intervention, while no significant improvements were observed in daily sedentary time (mean difference [MD]=−1.56, 95% CI −10.88 to 7.76; I²=0%; P=.74). These interventions were at least as effective as conventional interventions but did not show superiority. Compared with usual care, the interventions using wearable activity trackers only demonstrated a notable increase in daily step count over short-term follow-up (SMD=0.23, 95% CI 0.11-0.36; P<.001). As for body composition and physical function, there was low- to moderate-certainty evidence that the wearable activity tracker–based interventions did not have a greater impact on BMI (MD=0.40, 95% CI −0.08 to 0.89; P=.11), body fat (MD=0.67, 95% CI −0.54 to 1.87; P=.28), the timed up and go test (MD=0.14, 95% CI −0.87 to 1.16; P=.78), or the chair stand test (SMD=−0.31, 95% CI −0.62 to 0; P=.05). Conclusions: This systematic review and meta-analysis indicate that wearable activity tracker–based interventions were effective in enhancing physical activity with low to moderate certainty, but did not significantly impact body composition or physical function, with low to moderate certainty, among community-dwelling older adults, particularly immediately after intervention. This intervention showed a more pronounced impact when compared to usual care, rather than to conventional interventions, with low to moderate certainty. It is important to note that this intervention showed moderate-certainty evidence toward improving daily step count, supporting its sustained impact during short-term follow-up. Clinical Trial: PROSPERO CRD42024516900; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024516900

Background: Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various natural language processing tasks, particularly in text generation. However, their effectiveness in summarizing radiology report impressions remains uncertain. Objective: This study aims to evaluate the capability of nine LLMs, that is, Tongyi Qianwen, ERNIE Bot, ChatGPT, Bard, Claude, Baichuan, ChatGLM, HuatuoGPT, and ChatGLM-Med, in summarizing Chinese radiology report impressions for lung cancer. Methods: We collected 100 Chinese computed tomography (CT), positron emission tomography (PET)–CT, and ultrasound (US) reports each from Peking University Cancer Hospital and Institute. All these reports were from patients with suspected or confirmed lung cancer. Using these reports, we created zero-shot, one-shot, and three-shot prompts with or without complete example reports as inputs to generate impressions. We used both automatic quantitative evaluation metrics and five human evaluation metrics (completeness, correctness, conciseness, verisimilitude, and replaceability) to assess the generated impressions. Two thoracic surgeons (SZ and BL) and one radiologist (QL) compared the generated impressions with reference impressions, scoring them according to the five human evaluation metrics. Results: In the automatic quantitative evaluation, ERNIE Bot, Tongyi Qianwen, and Claude demonstrated the best overall performance in generating impressions for CT, PET-CT, and US reports, respectively. In the human semantic evaluation, ERNIE Bot outperformed the other LLMs in terms of conciseness, verisimilitude, and replaceability on CT impression generation, while its completeness and correctness scores were comparable to those of other LLMs. Tongyi Qianwen excelled in PET-CT impression generation, with the highest scores for correctness, conciseness, verisimilitude, and replaceability. Claude achieved the best conciseness, verisimilitude, and replaceability scores on US impression generation, and its completeness and correctness scores are close to the best results obtained by other LLMs. The generated impressions were generally complete and correct but lacked conciseness and verisimilitude. Although one-shot and few-shot prompts improved conciseness and verisimilitude, clinicians noted a significant gap between the generated impressions and those written by radiologists. Conclusions: Current LLMs can produce radiology impressions with high completeness and correctness but fall short in conciseness and verisimilitude, indicating they cannot yet fully replace impressions written by radiologists.

Background: People living with HIV/AIDS face multiple challenges that collectively impede their adaptation outcomes. These outcomes include quality of life (QoL), acceptance of illness, mental health (including symptoms of anxiety and depression), and antiretroviral therapy (ART) adherence. While existing evidence addresses specific challenges, it often overlooks the interactions among the various problems people living with HIV/AIDS encounter. The comprehensive-task disease management framework and positive self-management framework provide a theoretical basis for understanding the adaptation process. A culturally tailored, theory-based intervention may be necessary and effective in facilitating better adaptation outcomes for people living with HIV/AIDS. Objective: This study aimed to evaluate the effect of a hybrid intervention called AiCare (Adaptation intervention with Comprehensive-task disease management framework to achieve renormal life) on improving QoL, acceptance of illness, mental health (anxiety and depression), and ART adherence among people living with HIV/AIDS in China. Methods: We conducted a 2-arm randomized controlled trial, recruiting 92 people living with HIV/AIDS from an HIV clinic in Hunan, China. Participants were randomly assigned in a 1:1 ratio to either the control group (receiving standard care) or the intervention group (receiving AiCare in addition to standard care). All analyses were performed from an intention-to-treat perspective. Sociodemographic and HIV-specific clinical characteristics, along with key adaptation outcomes—including QoL, acceptance of illness, mental health (anxiety and depression), and ART adherence—were assessed at baseline (T0), post intervention (T1), and 3 months post intervention (T2). We used generalized estimating equation models and difference-in-difference analysis to evaluate the interventions’ effects. Results: The difference-in-difference model showed that at T1, the intervention group experienced significant improvements compared to the control group. QoL increased by 6.35 (95% CI 2.62-10.93, P=.001), acceptance of illness improved by 4.49 (95% CI 2.29-6.68, P<.001), and anxiety decreased by 2.15 (95% CI 1.19-3.11; P=.01). At T2, the intervention group’s improvement in QoL was not statistically significant (β 3.62, 95% CI –1.53 to 8.77; P=.17). However, acceptance of illness remained significantly improved by 3.65 (95% CI 1.22-6.08; P=.003), and anxiety decreased by 1.58 (95% CI 0.42-2.74; P=.007). No significant changes were observed in depression or ART adherence between the intervention and control groups. Feedback regarding the AiCare program indicated its acceptability and feasibility. Conclusions: The AiCare program demonstrated promising effects in improving disease adaptation outcomes among people living with HIV/AIDS, notably in enhancing QoL, fostering acceptance of illness, and mitigating anxiety symptoms. These findings underscore the hybrid program’s potential clinical utility to facilitate the adaptation of people living with HIV/AIDS. Trial Registration: Chinese Clinical Trial Registry ChiCTR2400087255; https://www.chictr.org.cn/showproj.html?proj=220729

Background: Patients’ electronic access to their health information can improve long-term health outcomes. Few studies have evaluated barriers that may limit access to portal health information before the COVID-19 pandemic such as preference for in-person visits, lack of perceived need to use a patient portal system, and lack of comfort or experience with computers. With the increased use of telehealth during the pandemic, patients’ comfort with portal applications and digital health literacy has improved. Objective: The purpose of this study was to assess the prevalence of portal use and factors associated with patients’ portal access after the COVID-19 pandemic. Methods: This study used data from the 2022 National Cancer Institute’s Health Information National Trends Survey (HINTS 6). Adult patients (aged ≥18 years) who responded to the survey question about patient portal access were included. A multivariate logistic regression analysis was performed to determine characteristics associated with portal access. Results: A total number of 5958 patients were included (weighted n=245,721,106), with a mean age of 48.2 (20.1) years and were mostly female (119,538,392/236,138,857, 50.6%) and white (167,163,482/227,232,636, 73.6%). Overall, 61.3% (150,722,178/245,721,106) of all respondents reported accessing portals over the last 12 months and 43.7% (82,620,907/188,860,031) used multiple portals. Most participants (135,011,661/150,104,795, 89.9%) reported using portals to access test results, followed by viewing clinical notes (104,541,142/149,867,276, 69.8%) downloading personal health information (47,801,548/150,017,130, 31.9%). The likelihood of portal use significantly increased by 24.9% points (95% CI 19.4-30.5) when patients were offered access to portals by health care providers or insurers compared with those not offered access or did not know if they were offered access. The likelihood of portal use also increased by 19.5% points (95% CI 15.1-23.9) among patients with a health care provider encouraging them to access portals, compared to patients who did not receive encouragement to do so. Having a college education versus education below college level and living in metropolitan areas versus nonmetropolitan regions increased the probability of portal use by 6.9% points (95% CI 3.1-10.8) and 6.9% points (95% CI 1.3-12.6), respectively. Of note, males (compared with females) and those of Hispanic background (compared with non-Hispanic individuals) were less likely to be offered portal access by 10.8% points (95% CI 6.3-15.2) and 6.9% points (95% CI 1.7-12.1), respectively. Conclusions: This study demonstrates that most Americans use patient portals, though certain patient populations such as those with less than college degree education or living in nonmetropolitan areas continue to face greater difficulty accessing them. Interventions targeted at equality in offering access to patient portals and encouraging patients to use them could advance equitable and widespread access to patient portals. Trial Registration:

Background: The effect of the COVID-19 pandemic on the well-being of dental professionals and patients has been difficult to track and quantify. X (formerly known as Twitter) proved to be a useful infoveillance tool for tracing the impact of the COVID-19 pandemic worldwide. Objective: This study aims to investigate the use of X to track COVID-19 infections and deaths associated with dental practices. Methods: English Tweets reporting infections or deaths associated with the dental practice were collected from January 1, 2020, to March 31, 2021. Tweets were searched manually using the X Pro search engine (previously known as TweetDeck [X Corp], Twitter Inc, and TweetDeck Ltd) and automatically using a tweet crawler on the X Academic Research application programming interface. Queries included keywords on infection or death of dental staff and patients caused by COVID-19. Tweets registering events on infection or death of dentists, dental staff, and patients as part of their conversation were included. Results: A total of 5641 eligible tweets were retrieved. Of which 1583 (28.1%) were deemed relevant after applying the inclusion and exclusion criteria. Of the relevant tweets, 311 (19.6%) described infections at dental practices, where 1168 (86.9%) infection cases were reported among dentists, 134 (9.9%) dental staff, and 41 (3.1%) patients. The majority of reported infections occurred in the United States, India, and Canada, affecting individuals aged 20-51 years. Among the 600 documented deaths, 253 (42.2%) were dentists, 22 (3.7%) were dental staff, and 7 (1.2%) were patients. The countries with the highest number of deaths were the United States, Pakistan, and India, with an affected age range of 23-83 years. Conclusions: The data suggest that analyses of X information in populations of affected areas may provide useful information regarding the impact of a pandemic on the dental profession and demonstrate a correlation with suspected and confirmed infection or death cases. Platform X shows potential as an early predictor for disease spread. However, further research is required to confirm its validity.

Background: Floodlight Open is a global, open-access, fully remote, digital-only study designed to understand the drivers and barriers in deployment and persistence of use of a smartphone app for measuring functional impairment in a naturalistic setting and broad study population. Objective: This study aims to assess measurement equivalence properties of the Floodlight Open app across operating system (OS) platforms, OS versions, and smartphone device models. Methods: Floodlight Open enrolled adult participants with and without self-declared multiple sclerosis (MS). The study used the Floodlight Open app, a “bring-your-own-device” (BYOD) solution that remotely measured MS-related functional ability via smartphone sensor–based active tests. Measurement equivalence was assessed in all evaluable participants by comparing the performance on the 6 active tests (ie, tests requiring active input from the user) included in the app across OS platforms (iOS vs Android), OS versions (iOS versions 11-15 and separately Android versions 8-10; comparing each OS version with the other OS versions pooled together), and device models (comparing each device model with all remaining device models pooled together). The tests in scope were Information Processing Speed, Information Processing Speed Digit-Digit (measuring reaction speed), Pinching Test (PT), Static Balance Test, U-Turn Test, and 2-Minute Walk Test. Group differences were assessed by permutation test for the mean difference after adjusting for age, sex, and self-declared MS disease status. Results: Overall, 1976 participants using 206 different device models were included in the analysis. Differences in test performance between subgroups were very small or small, with percent differences generally being ≤5% on the Information Processing Speed, Information Processing Speed Digit-Digit, U-Turn Test, and 2-Minute Walk Test; <20% on the PT; and <30% on the Static Balance Test. No statistically significant differences were observed between OS platforms other than on the PT (P<.001). Similarly, differences across iOS or Android versions were nonsignificant after correcting for multiple comparisons using false discovery rate correction (all adjusted P>.05). Comparing the different device models revealed a statistically significant difference only on the PT for 4 out of 17 models (adjusted P≤.001-.03). Conclusions: Consistent with the hypothesis that smartphone sensor–based measurements obtained with different devices are equivalent, this study showed no evidence of a systematic lack of measurement equivalence across OS platforms, OS versions, and device models on 6 active tests included in the Floodlight Open app. These results are compatible with the use of smartphone-based tests in a bring-your-own-device setting, but more formal tests of equivalence would be needed.

Background: Digital health care self-monitoring has gained prominence as a tool to address various challenges in health care, including patient autonomy, data-informed decision-making, and organizational improvements. However, integrating self-monitoring solutions across a diverse ecosystem of stakeholders—patients, health care providers, policy makers, and industry—can be complicated by differing priorities and needs. Objective: This study aimed to identify and categorize the distinct purposes underpinning the use of digital health care self-monitoring. By mapping these purposes, the research seeks to clarify how technology design and implementation can be better aligned with stakeholder expectations, thereby enhancing adoption and impact. Methods: A qualitative design was used, drawing on 31 in-depth, semistructured interviews conducted with stakeholders in the Swedish health care ecosystem. Participants included patients, advocacy groups, health care professionals, policy makers, pharmaceutical representatives, and technology developers. Data were analyzed thematically using an inductive coding approach supported by NVivo 12 (Lumivero). Emerging themes were refined through iterative discussion among the research team and validated by presentation to health care practitioners. Results: A total of 8 distinct purposes of digital health care self-monitoring emerged: (1) emancipate (enhance patient autonomy), (2) learn (understand health behaviors), (3) improve (enhance patient health), (4) engage (bolster patient involvement), (5) control (manage adherence and symptoms), (6) evaluate (assess health parameters), (7) innovate (advance interventions and processes), and (8) generate (drive new initiatives). These purposes form three categories of value creation: (1) improving the patient-provider link, (2) leveraging big data analytics for knowledge creation, and (3) using digital infrastructure to develop new care processes. Conclusions: Our findings demonstrate that digital health care self-monitoring serves multifaceted aims, ranging from individual patient empowerment to ecosystem-wide innovation. Designing and implementing these tools with an explicit understanding of all stakeholders’ “why” can help address potential conflicts (eg, balancing patient autonomy with clinical control) and facilitate more holistic adoption. Ultimately, this study underscores the importance of clear, purpose-driven approaches to promote better health outcomes, knowledge generation, and care process improvements.

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.