Nevin Manimala

JMIR Public Health Surveill. 2025 Jul 18;11:e76195. doi: 10.2196/76195.

ABSTRACT

BACKGROUND: Unintentional injury is a leading cause of childhood morbidity and mortality worldwide. In China, real-world implementation of child injury prevention efforts remains inadequate due to constrained workforce capacity and a lack of operational frameworks.

OBJECTIVE: This study aims to assess the effectiveness of a mobile health (mHealth) intervention, the Intelligent Child Unintentional-Injury Reduction & Education (iCURE) project, embedded within China’s National Basic Public Health Service Program. The intervention relies on a WeChat (Tencent) service account for caregivers and a web-based platform for health care providers to deliver standardized unintentional injury prevention strategies for young children. Key features of the program include interactive questions and answers, injury risk assessment with instant feedback, a tailored injury prevention knowledge disseminator, and regular reminders to caregivers.

METHODS: A double-blind, 12-month follow-up, cluster randomized controlled trial will be implemented in Changsha, Hunan Province, China. Caregivers of children aged ≤5 years will be recruited. Randomization will be conducted at the street or town level. The control group will receive routine safety education, while the intervention group will receive both routine safety education and the iCURE mHealth intervention focused on unintentional injury prevention and delivered via WeChat. Data will be collected at baseline and every 3 months during the study period. The primary outcome is 12-month incidence of unintentional injuries among children, including minor injuries and as reported by caregivers. Secondary outcomes include children’s injury risk level and caregiver supervision behaviors assessed using a standard questionnaire. Data analysis will be conducted using generalized linear mixed models with a Poisson link function and generalized estimating equations to assess the effectiveness of the iCURE intervention, following intention-to-treat principles. Sensitivity analyses will be conducted with per-protocol principles and excluding participants with missing primary outcomes.

RESULTS: As of May 2025, a total of 6701 participants have been successfully enrolled and baseline data were collected for all participants. Of those enrolled, 87.2% (5842/6,701) completed the first follow-up assessment.

CONCLUSIONS: This trial will examine the effectiveness of an intelligent mHealth intervention for child unintentional injury prevention building on China’s National Basic Public Health Service Program. If successful, the iCURE intervention may provide a cost-effective strategy for child injury prevention in low- and middle-income countries.

PMID:40680288 | DOI:10.2196/76195

Nurs Res. 2025 Jul 14. doi: 10.1097/NNR.0000000000000852. Online ahead of print.

ABSTRACT

BACKGROUND: Nurses experience high rates of mental health stressors. Mental health support services can mitigate the burden of these stressors and mental health sequelae, but nurses may not access them due to stigma, unavailability, or concerns regarding professional standing.

OBJECTIVE: To examine the association between nurse characteristics and perceived need for and utilization of mental health services and identify barriers to nurses accessing mental health services.

METHODS: Cross-sectional survey data were analyzed from 367 nurses in one large urban health system. Descriptive statistics and multinomial logistic regression were used to examine demographic, workplace, and mental health characteristics that were independently associated with perceived need for and utilization of mental health services.

RESULTS: Nurses who reported experiences of verbal abuse, anxiety, or burnout and who were younger and partnered were more likely to have utilized mental health services and have perceived they would benefit from these services. The most prevalent barriers to care included not knowing how to find a provider, inconvenient hours, and embarrassment or concerns about judgment from others.

DISCUSSION: Several barriers prevent nurses who perceive a need for mental health services from accessing them. Interventions targeting these barriers may help mitigate the burden of mental health conditions in this population.

PMID:40680286 | DOI:10.1097/NNR.0000000000000852

Nurs Res. 2025 Jul 14. doi: 10.1097/NNR.0000000000000848. Online ahead of print.

ABSTRACT

BACKGROUND: Computable phenotyping is a data science method that systematically synthesizes clinical attributes, such as a disease, condition, or patient cohort, enabling a database to be queried for entries matching these characteristics. Developing computable phenotypes will enhance current clinical and research efforts and is foundational for effective nurse scholar participation in future data science endeavors, such as artificial intelligence (AI) and machine learning (ML) research.

OBJECTIVE: (a) Present a foundational, disease-agnostic framework for systematic computable phenotype construction; (b) demonstrate the framework used by exploring the following question: “Does early pubertal timing increase the risk of developing type II diabetes in males?”; and (c) outline the methodologic utility and limitations of computable phenotyping for nursing research.

METHODS: A proof-of-concept pilot project explored computable phenotype research utility by querying the TriNetX© de-identified health record database. Various computable phenotypes were constructed to retrieve complete case frequency counts of specific health records for children experiencing puberty. These retrieved records allowed for quantifying type 2 diabetes (T2D) risk by comparing children diagnosed with precocious puberty (medically diagnosed early puberty) to those without an abnormal puberty diagnosis. A translational science lens informed the extraction and synthesis of the underlying scientific and operational principles relevant to systematic computable phenotyping.

RESULTS: A six-step, disease-agnostic, computable phenotyping framework is synthesized for nurse researchers and clinicians to leverage “big data” applications in their work. The puberty case example-illustrating foundational use of the framework-suggests that males with precocious puberty may be six times more likely to develop T2D when 14-18 years old than those without diagnosed early puberty. The framework provides a foundation for sophisticated statistical analyses, such as leveraging computable phenotypes in multivariate modeling and machine learning algorithms.

DISCUSSION: The six-step, computable phenotype framework will introduce nurse scholars and clinicians to leverage data science principles in real-world interfaces. Applications using the framework can include generating and testing epidemiologic hypotheses, identifying participants for research with specific clinical attributes, deploying statistical models for health care monitoring and decision-making, and participating in future research on AI and ML algorithms. The puberty case example generates foundational evidence to justify future puberty research.

PMID:40680284 | DOI:10.1097/NNR.0000000000000848

JMIR Cancer. 2025 Jul 18;11:e72862. doi: 10.2196/72862.

ABSTRACT

BACKGROUND: Digital health portals are online platforms allowing individuals to access their personal information and communicate with health care providers. While digital health portals have been associated with improved health outcomes and more streamlined health care processes, their impact on individuals living with or beyond cancer remains underexplored.

OBJECTIVE: This scoping review aimed to (1) identify the portal functionalities reported in studies involving individuals living with or beyond cancer, as well as the outcomes assessed, and (2) explore the diversity of participant characteristics and potential factors associated with portal use.

METHODS: We conducted a scoping review in accordance with the JBI methodology (formerly the Joanna Briggs Institute) and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. We included primary research studies published between 2014 and 2024 that involved participants living with or beyond cancer, had access to personal health information, and assessed at least one outcome related to health or the health care system. We searched the Embase, Web of Science, MEDLINE (Ovid), and CINAHL Plus with Full Text databases. Five reviewers independently screened all titles, abstracts, and full texts in duplicate using Covidence. We extracted data on study design, participant characteristics, portal functionalities, outcomes assessed, and PROGRESS-Plus (place of residence; race, ethnicity, culture, or language; occupation; gender or sex; religion; education; socioeconomic status; and social capital-Plus) equity factors.

RESULTS: We included 44 studies; most were conducted in the United States (n=30, 68%) and used quantitative (n=23, 52%), mixed methods (n=11, 25%), or qualitative (n=10, 23%) designs. The most common portal features were access to test results (28/44, 64%) and secure messaging (30/44, 68%). Frequently reported services included appointment-related functions (19/44, 43%), educational resources (13/44, 30%), and prescription management features (11/44, 25%). Behavioral and technology-related outcomes were the most frequently assessed (37/44, 84%), followed by system-level (19/44, 43%), psychosocial (16/44, 36%), and clinical outcomes (5/44, 11%). Overall, 43% (19/44) of the studies addressed PROGRESS-Plus factors. Age was the most frequently reported (13/19, 68%), followed by socioeconomic status (10/19, 53%), race or ethnicity (7/19, 37%), and gender or sex (7/19, 37%). Social capital (2/19, 11%), occupation (1/19, 5%), and disability (1/19, 5%) were rarely considered, and religion was not reported in any study.

CONCLUSIONS: While digital health portals enhance patient engagement, their clinical impact and equity implications remain insufficiently evaluated. We found disparities in functionalities, outcomes, and PROGRESS-Plus representation. To promote equitable benefits, future studies should adopt inclusive designs and evaluation strategies that address diverse outcomes and integrate social determinants of health.

PMID:40680274 | DOI:10.2196/72862

J Am Acad Orthop Surg Glob Res Rev. 2025 Jul 17;9(7). doi: 10.5435/JAAOSGlobal-D-25-00195. eCollection 2025 Jul 1.

ABSTRACT

BACKGROUND: Climate change is a global health emergency, with substantial carbon emissions coming from health care. This study compares the carbon footprint of in-person versus virtual orthopaedic care at a large, urban, academic healthcare system.

METHODS: Data were abstracted from the billing and claims database for orthopaedic clinic visits from 2018 to 2023 at a large, urban, academic medical center and its suburban satellite clinics. Carbon footprint per in-person visit was determined by combining emissions from supplies, facility energy use, and patient travel. The reduction in emissions of virtual visits was calculated compared with if all visits occurred in person.

RESULTS: Overall, 508,394 orthopaedic clinic visits (94.3% in-person, 5.7% virtual) were recorded. The average in-person visit resulted in 7.12 vs. 0.026 kg CO2e for the average virtual visit. Actual carbon emissions were estimated to be 3,411,206 kg CO2e compared with 3,714,565 kg if all visits occurred in person (8.2% reduction). Most emissions (99.8%) were attributed to patient travel, with 0.2% coming from supplies and <0.1% from facility energy use. The peak of the COVID-19 pandemic in 2020 saw the greatest reduction in carbon emissions at 19.5%, with emissions increasing each year thereafter (8.3% reduction in 2023).

CONCLUSION: The carbon footprint of clinic-based orthopaedic care is large and can be reduced by transitioning from in person to virtual care. Although virtual orthopaedic care has limitations, the environmental benefits are clear. Further research into virtual outpatient orthopaedic care should consider environmental impacts in addition to safety, effectiveness, and patient satisfaction.

PMID:40680267 | DOI:10.5435/JAAOSGlobal-D-25-00195

J Am Acad Orthop Surg Glob Res Rev. 2025 Jul 2;9(7). doi: 10.5435/JAAOSGlobal-D-24-00263. eCollection 2025 Jul 1.

ABSTRACT

INTRODUCTION: To assess ballistic proximal femur fracture outcomes in comparison with proximal femur fractures sustained by blunt mechanisms. We hypothesized that ballistic proximal femur fractures would have higher rates of infection, nonunion, and compartment syndrome than nonballistic fractures.

METHODS: A retrospective cohort was collected from the electronic medical record of a single, Level I, trauma center over a 10-year period (2013 to 2022) using Current Procedural Terminology codes. All consecutive adult patients with ballistic proximal third femur fractures (femoral neck, intertrochanteric, subtrochanteric) managed with surgical fixation were identified. A comparison group of proximal femur fractures sustained by nonballistic mechanisms was collected from consecutive patients in a 3-year period (2020 to 2022), creating a 2:1 nonballistic-to-ballistic fracture ratio. Exclusion criteria consisted of younger than 18 years or older than 65 years, primary fixation of total/hemi hip arthroplasty, primary pathologic fractures, and fractures across existing prosthesis. The primary outcomes measured include concomitant genitourinary injury, computed tomographic angiography with abnormality, vascular injury requiring repair, soft-tissue reconstruction, thigh compartment syndrome, length of stay, fracture-related infection, revision surgery to promote bone healing, and implant failure.

RESULTS: A total of 411 patients were included with 137 (33%) sustaining ballistic proximal femur fractures. Most blunt fractures were closed (86.8%), whereas most ballistic fractures were Gustilo Anderson type 1 open fractures (81.7%). The individuals in the ballistic cohort were more likely to have vascular injury requiring surgical intervention (8.8% vs. 1.1%, P < 0.001), computed tomographic angiography with abnormality (10.9% vs. 1.1%, P < 0.001), compartment syndrome (7.3% vs. 0.7%, P < 0.001), concomitant GU injury (12.4% vs. 1.8%, P < 0.001), and deep vein thrombosis (5.1% vs. 1.5%, P = 0.048).

CONCLUSION: Ballistic proximal femur fractures are associated with a higher risk of developing complications associated with trauma to nearby vascular structures and concomitant genitourinary structures. The rates of infection, revision surgery to promote bone healing, and implant failure were similar between the ballistic and nonballistic proximal femur fractures.

PMID:40680257 | DOI:10.5435/JAAOSGlobal-D-24-00263

JMIR Form Res. 2025 Jul 18;9:e74411. doi: 10.2196/74411.

ABSTRACT

BACKGROUND: Digital interventions have been proposed as a solution to meet the growing demand for mental health support. Large language models (LLMs) have emerged as a promising technology for creating more personalized and adaptive mental health chatbots. While LLMs generate responses based on statistical patterns in training data rather than through conscious reasoning, they can be designed to support important psychological processes. Prospection-the ability to envision and plan for future outcomes-represents a transdiagnostic process altered across various mental health conditions that could be effectively targeted through such interventions. We designed “Future Me,” an LLM-powered chatbot designed to facilitate future-oriented thinking and promote goal pursuit using evidence-based interventions including visualization, implementation intentions, and values clarification.

OBJECTIVE: This study aims to understand how users engage with Future Me, evaluate its effectiveness in supporting future-oriented thinking, and assess its acceptability across different populations, with particular attention to postgraduate students’ stress management needs. We also seek to identify design improvements that could enhance the chatbot’s ability to support users’ mental well-being.

METHODS: In total, 2 complementary studies were conducted. Study 1 (n=20) examined how postgraduate students used Future Me during a single guided session, followed by semistructured interviews. Study 2 (n=14) investigated how postgraduate students interacted with Future Me over a 1-week period, with interviews before and after usage. Both studies analyzed conversation transcripts and interview data using thematic analysis to understand usage patterns, perceived benefits, and limitations.

RESULTS: Across both studies, participants primarily engaged with Future Me to discuss career or education goals, personal obstacles, and relationship concerns. Users valued Future Me’s ability to provide clarity around goal-setting (85% of participants), its nonjudgmental nature, and its 24/7 accessibility (58%). Future Me effectively facilitated self-reflection (80%) and offered new perspectives (70%), particularly for broader future-oriented concerns. However, both studies revealed limitations in the chatbot’s ability to provide personalized emotional support during high-stress situations, with participants noting that responses sometimes felt formulaic (50%) or lacked emotional depth. Postgraduate students specifically emphasized the need for greater context awareness during periods of academic stress (58%). Overall, 57% of requests occurred outside office hours, dropping from 40 on day 1 to 12 by day 7.

CONCLUSIONS: Future Me demonstrates promise as an accessible tool for promoting prospection skills and supporting mental well-being through future-oriented thinking. However, effectiveness appears context-dependent, with prospection techniques more suitable for broader life decisions than acute stress situations. Future development should focus on creating more adaptive systems that can adjust their approach based on the user’s emotional state and immediate needs. Rather than attempting to replicate human therapy entirely, chatbots like Future Me may be most effective when designed as complementary tools within broader support ecosystems, offering immediate guidance while facilitating connections to human support when needed.

PMID:40680255 | DOI:10.2196/74411

J Gerontol A Biol Sci Med Sci. 2025 Jul 18:glaf157. doi: 10.1093/gerona/glaf157. Online ahead of print.

ABSTRACT

BACKGROUND: Aim of the study was a comparative analysis of different epigenetic clocks with regard to their ability to predict a future onset of the Metabolic Syndrome (MetS). In addition, cross-sectional relationships between epigenetic age measures and MetS were investigated.

METHODS: MetS was diagnosed in participants of the Berlin Aging Study II at baseline (n = 1,671, mean age 68.8 ± 3.7 years, 51.6% women) and at follow-up (n = 1,083; 7.4 ± 1.5 years later). DNA methylation age acceleration (DNAmAA) was calculated for a total of ten epigenetic clocks at baseline. In addition, DunedinPACE, a DNAm-based measure of the pace of aging, was calculated. The relationship between MetS, DNAmAA and DunedinPACE was investigated by fitting regression models adjusted for potential confounders and calculating receiver operating characteristic statistics.

RESULTS: Among all biomarkers investigated, DunedinPACE was the only DNAm-based predictor that was significantly associated with incident MetS at follow-up on average 7.4 years later (OR: 9.84, p = 0.028). Logistic regression models predicting MetS that either included solely clinical parameters or solely epigenetic clock estimates (DNAmAA) or DunedinPACE revealed that GrimAge DNAmAA had an area under the curve most comparable to the model considering clinical variables only. Cross-sectional differences between participants with and without MetS remained statistically significant for DunedinPACE only after covariate adjustment (baseline: β = 0.042, follow-up: β = 0.031, p < 0.0001 in both cases).

CONCLUSION: Comparison of epigenetic clocks in relation to MetS showed strong and consistent associations with DunedinPACE. Our results highlight the potential of using certain DNAm-based measures of biological ageing in predicting the onset of clinical outcomes, such as MetS.

PMID:40680238 | DOI:10.1093/gerona/glaf157

J Med Internet Res. 2025 Jul 18;27:e76215. doi: 10.2196/76215.

ABSTRACT

BACKGROUND: Machine learning (ML) models may offer greater clinical utility than conventional risk scores, such as the Thrombolysis in Myocardial Infarction (TIMI) and Global Registry of Acute Coronary Events (GRACE) risk scores. However, there is a lack of knowledge on whether ML or traditional models are better at predicting the risk of major adverse cardiovascular and cerebrovascular events (MACCEs) in patients with acute myocardial infarction (AMI) who have undergone percutaneous coronary interventions (PCI).

OBJECTIVE: The aim of this study is to systematically review and critically appraise studies comparing the performance of ML models and conventional risk scores for predicting MACCEs in patients with AMI who have undergone PCI.

METHODS: Nine academic and electronic databases including PubMed, CINAHL, Embase, Web of Science, Scopus, ACM, IEEE, Cochrane, and Google Scholar were systematically searched from January 1, 2010, to December 31, 2024. We included studies of patients with AMI who underwent PCI, and predicted MACCE risk using ML algorithms or conventional risk scores. We excluded conference abstracts, gray literature, reviews, case reports, editorials, qualitative studies, secondary data analyses, and non-English publications. Our systematic search yielded 10 retrospective studies, with a total sample size of 89,702 individuals. Three validation tools were used to assess the validity of the published prediction models. Most included studies were assessed as having a low overall risk of bias.

RESULTS: The most frequently used ML algorithms were random forest (n=8) and logistic regression (n=6), while the most used conventional risk scores were GRACE (n=8) and TIMI (n=4). The most common MACCEs component was 1-year mortality (n=3), followed by 30-day mortality (n=2) and in-hospital mortality (n=2). Our meta-analysis demonstrated that ML-based models (area under the receiver operating characteristic curve: 0.88, 95% CI 0.86-0.90; I²=97.8%; P<.001) outperformed conventional risk scores (area under the receiver operating characteristic curve: 0.79, 95% CI 0.75-0.84; I²=99.6%; P<.001) in predicting mortality risk among patients with AMI who underwent PCI. Heterogeneity across studies was high. Publication bias was assessed using a funnel plot. The top-ranked predictors of mortality in both ML and conventional risk scores were age, systolic blood pressure, and Killip class.

CONCLUSIONS: This review demonstrated that ML-based models had superior discriminatory performance compared to conventional risk scores for predicting MACCEs in patients with AMI who had undergone PCI. The most commonly used predictors were confined to nonmodifiable clinical characteristics. Therefore, health care professionals should understand the advantages and limitations of ML algorithms and conventional risk scores before applying them in clinical practice. We highlight the importance of incorporating modifiable factors-including psychosocial and behavioral variables-into prediction models for MACCEs following PCI in patients with AMI. In addition, further multicenter prospective studies with external validation are required to address validation limitations.

PMID:40680235 | DOI:10.2196/76215

Physiol Rep. 2025 Jul;13(14):e70463. doi: 10.14814/phy2.70463.

ABSTRACT

Selective androgen receptor modulators (SARMs) are important hypertrophic molecules that are potential treatments for many types of myopathy and osteopathy. This study aimed to determine if the SARM RAD140 had additive effects on muscle hypertrophy when combined with the functional overloading (FO) of the plantaris muscle. Male, Sprague-Dawley rats (n = 10 rats/group) were randomly selected into one of four treatment groups: (1) RAD140-FO, (2) RAD140-Control, (3) Vehicle-Control, or (4) Vehicle-FO. RAD140 groups received drugs through drinking water, and control groups received only Vehicle (methylcellulose). Standard rat chow and water were provided ad libitum. Muscle weights of the triceps-surae group and muscle fiber cross-sectional area (CSA) were measured. Muscle weight analysis showed a marked increase in RAD140-FO groups but was not statistically different from the Vehicle-FO group. CSA results indicated similar findings; however, RAD140-Control showed significantly elevated CSA compared to Vehicle-Control. The tibial microarchitecture was analyzed using micro computed tomography. RAD140 did not impact cortical or trabecular bone structural morphometric properties following 14 days of treatment. The data presented here show the potential of RAD140 to stimulate muscle hypertrophy in young healthy rats.

PMID:40680216 | DOI:10.14814/phy2.70463