%0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e65490 %T An In-Person and Online Intervention for Parkinson Disease (UPGRADE-PD): Protocol for a Patient-Centered and Culturally Tailored 3-Arm Crossover Trial %A Elpidoforou,Michail %A Grimani,Irene %A Papadopoulou,Marianna %A Papagiannakis,Nikolaos %A Bougea,Anastasia %A Simitsi,Athina-Maria %A Sfikas,Evangelos %A Alexandratou,Ioanna %A Alefanti,Ioanna %A Antonelou,Roubina %A Koros,Christos %A Mavroyianni,Ioanna %A Chrysovitsanou,Chrysa %A Stefanis,Leonidas %A Bakalidou,Daphne %+ Laboratory of Neuromuscular and Cardiovascular Study of Motion – LANECASM, Department of Physiotherapy, University of West Attica, Ag. Spyridonos Str., Egaleo, Athens, 12243, Greece, 30 6936724706, melpidoforou@uniwa.gr %K Parkinson disease %K dance %K cultural tailoring %K patient-centeredness %K quality of life %K frailty %K sarcopenia %D 2025 %7 2.5.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Dance for Parkinson’s Disease (DfPD) is a dance program for individuals with Parkinson disease (PD). There is a lack of knowledge about the effect of this program on frailty and sarcopenia experienced by patients with PD. In addition, no randomized controlled trial to date has investigated either the possible differential effects of in-person versus online DfPD or the possible effects of DfPD on clinical parameters in Greek patients with PD. Objective: We aimed to assess the efficacy, safety, and feasibility of a culturally tailored and patient-centered DfPD program offered both in-person and online to Greek patients with early- to midstage PD. Methods: This is a 3-arm crossover randomized controlled trial (in-person DfPD vs online DfPD vs control) of UPGRADE-PD (Upbeating Greek Application of Dance in Parkinson’s Disease). The experimental period will be 10 months, including three 2-month interventional periods of two 60-minute dance classes per week for each group (in-person DfPD vs online DfPD) versus a control group (nonintervention group), and two 2-month washout periods between each group for 40 Greek patients with early- to midstage PD. Assessments will be performed face-to-face at baseline and at the end of each study period and will include quality of life, fatigue, depressive symptoms, stress, anxiety, sarcopenia, frailty, balance, cognitive functions, movement and nonmovement PD symptoms, and BMI. Safety, feasibility, and patient satisfaction for each dance intervention (in-person DfPD vs online DfPD) will be assessed as well. Results: The study protocol was approved by the Medical Ethics Committee of the Eginition University Hospital in September 2022 and the Research and Ethics Committee of the University of West Attica in October 2023 and funded in September 2023. The first participant was enrolled in April 2023, and the trial is currently ongoing and will conclude in September 2024. Conclusions: The results of this study are expected to show the possible differential effect of a patient-centered and culturally tailored in-person vs online DfPD intervention on several movement and nonmovement symptoms, as well as on quality of life, sarcopenia, and frailty in people living with PD in Greece. Trial Registration: ClinicalTrials.gov NCT06220084; https://clinicaltrials.gov/study/NCT06220084 International Registered Report Identifier (IRRID): DERR1-10.2196/65490 %M 40314994 %R 10.2196/65490 %U https://www.researchprotocols.org/2025/1/e65490 %U https://doi.org/10.2196/65490 %U http://www.ncbi.nlm.nih.gov/pubmed/40314994 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e59631 %T Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and Medical Record Data: Machine Learning Approach %A Waugh,Mihyun Lim %A Mills,Tyler %A Boltin,Nicholas %A Wolf,Lauren %A Parker,Patti %A Horner,Ronnie %A Wheeler II,Thomas L %A Goodwin,Richard L %A Moss,Melissa A %K cytokines %K inflammatory response %K medical record %K pelvic organ prolapse %K polypropylene mesh %K supervised machine learning models %K polypropylene %K mesh surgery %K surgical outcome %K cost-efficiency %K risk factor %K efficacy %K health care data %K female %K informed decision-making %K patient care %K digital health %D 2025 %7 1.5.2025 %9 %J JMIR Form Res %G English %X Background: Transvaginal insertion of polypropylene mesh was extensively used in surgical procedures to treat pelvic organ prolapse (POP) due to its cost-efficiency and durability. However, studies have reported a high rate of complications, including mesh exposure through the vaginal wall. Developing predictive models via supervised machine learning holds promise in identifying risk factors associated with such complications, thereby facilitating better informed surgical decisions. Previous studies have demonstrated the efficacy of anticipating medical outcomes by employing supervised machine learning approaches that integrate patient health care data with laboratory findings. However, such an approach has not been adopted within the realm of POP mesh surgery. Objective: We examined the efficacy of supervised machine learning to predict mesh exposure following transvaginal POP surgery using 3 different datasets: (1) patient medical record data, (2) biomaterial-induced blood cytokine levels, and (3) the integration of both. Methods: Blood samples and medical record data were collected from 20 female patients who had prior surgical intervention for POP using transvaginal polypropylene mesh. Of these subjects, 10 had experienced mesh exposure through the vaginal wall following surgery, and 10 had not. Standardized medical record data, including vital signs, previous diagnoses, and social history, were acquired from patient records. In addition, cytokine levels in patient blood samples incubated with sterile polypropylene mesh were measured via multiplex assay. Datasets were created with patient medical record data alone, blood cytokine levels alone, and the integration of both data. The data were split into 70% and 30% for training and testing sets, respectively, for machine learning models that predicted the presence or absence of postsurgical mesh exposure. Results: Upon training the models with patient medical record data, systolic blood pressure, pulse pressure, and a history of alcohol usage emerged as the most significant factors for predicting mesh exposure. Conversely, when the models were trained solely on blood cytokine levels, interleukin (IL)-1β and IL-12 p40 stood out as the most influential cytokines in predicting mesh exposure. Using the combined dataset, new factors emerged as the primary predictors of mesh exposure: IL-8, tumor necrosis factor-α, and the presence of hemorrhoids. Remarkably, models trained on the integrated dataset demonstrated superior predictive capabilities with a prediction accuracy as high as 94%, surpassing the predictive performance of individual datasets. Conclusions: Supervised machine learning models demonstrated improved prediction accuracy when trained using a composite dataset that combined patient medical record data and biomaterial-induced blood cytokine levels, surpassing the performance of models trained with either dataset in isolation. This result underscores the advantage of integrating health care data with blood biomarkers, presenting a promising avenue for predicting surgical outcomes in not only POP mesh procedures but also other surgeries involving biomaterials. Such an approach has the potential to enhance informed decision-making for both patients and surgeons, ultimately elevating the standard of patient care. %R 10.2196/59631 %U https://formative.jmir.org/2025/1/e59631 %U https://doi.org/10.2196/59631 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e66815 %T Integrating a Mobile App to Enhance Atrial Fibrillation Care: Key Insights From an Implementation Study Guided by the Consolidated Framework for Implementation Research %A Hewage,Sumudu %A Kularatna,Sanjeewa %A Parsonage,William %A Walters,Tomos %A McPhail,Steven %A Brain,David %A Allen,Michelle J %+ Australian Centre for Health Services Innovation, Queensland University of Technology, 88, Musk Avenue, Kelvin Grove, 4059, Australia, 61 073388 ext 6670, sumuduavanthi@gmail.com %K mobile health apps %K digital health integration %K health care innovation %K technology adoption %K cardiac rehabilitation %K lifestyle modification %K implementation science %K consolidated framework for implementation framework %K mHealth %K mobile health %K app %K digital health %K smartphone %K eHealth %K telehealth %K telemedicine %K digital %K technology %K CFIR %K implementation research %K cardiac %K rehabilitation %K cardiology %K atrial fibrillation %K Australia %K interview %D 2025 %7 30.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Despite the growing use of mobile health apps in managing chronic heart disease, their integration into routine care remains challenging due to dynamic, context-specific barriers. Objective: This study aimed to identify the key enablers and challenges of implementing a mobile app for cardiac rehabilitation and healthy lifestyles in patients with atrial fibrillation at an Australian cardiology clinic. Methods: We interviewed both clinicians and patients to understand their perspectives about the mobile app and what factors affected the implementation. The two semistructured interview guides used, one for clinicians and one for patients, were developed based on the Consolidated Framework for Implementation Research (CFIR) and nonadoption abandonment, scale-up, spread, and sustainability complexity assessment tool. All interviews were recorded and transcribed, and the transcripts were analyzed inductively to generate codes using a constructionist perspective. These codes were subsequently mapped onto the constructs within the CFIR across its five domains. This framework analysis was followed by examining the interconnections among the constructs to understand their collective impact on the implementation process, thereby identifying key enablers and challenges for the integration efforts. Results: We interviewed 24 participants including 18 patients, whose mean age was 69 (SD 9.2) years, and 6 clinicians, comprising 4 specialist cardiac electrophysiologists and 2 nurses. Patient engagement with the app varied: 3 participants completed the cardiac rehabilitation plan, 1 participant was still actively engaged, 2 participants had partial use, 10 participants downloaded but never used the app, and 2 participants did not download the app. We identified a complex interplay between key determinants across all five CFIR domains, collectively impacting two main elements in the implementation process: (1) acceptability and user engagement with the app and (2) the clinic’s implementation readiness. The app was more likely to be accepted and used by patients who needed to establish healthy lifestyle habits. Those with established healthy lifestyle habits did not indicate that the app provided sufficient added value to justify adoption. Interoperability with other devices and design issues, for example, limited customization options, also negatively impacted the uptake. The clinic’s implementation readiness was limited by various challenges including limited staff availability, insufficient internal communication processes, the absence of an implementation evaluation plan, and lack of clarity around who is funding the app’s use beyond the initial trial. Despite the clinician’s overall inclination toward technology use, diverse opinions on the evidence for short-term cardiac rehabilitation programs in atrial fibrillation critically reduced their commitment to app integration. Conclusions: Mobile health apps have seen rapid expansion and offer clear benefits, yet their integration into complex health systems remains challenging. Whilst our findings are from a single app implementation, they highlight the importance of embedding contextual analysis and proactive strategic planning in the integration process. %M 40306646 %R 10.2196/66815 %U https://www.jmir.org/2025/1/e66815 %U https://doi.org/10.2196/66815 %U http://www.ncbi.nlm.nih.gov/pubmed/40306646 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e71086 %T Real-World Mobile Health Implementation and Patient Safety: Multicenter Qualitative Study %A Su,Jing Jing %A Chan,Michelle Hui Shan %A Ghisi,Gabriela Lima de Melo %A Kwan,Rick Yiu Cho %A Wong,Arkers Kwan Ching %A Lin,Rose %A Yeung,Jerry Wing Fai %A He,Qijun %A Pepera,Garyfallia %A Batalik,Ladislav %+ Department of Physiotherapy and Rehabilitation, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, 62500, Czech Republic, 420 532233123, batalik.ladislav@fnbrno.cz %K mHealth %K mobile health %K patient safety %K qualitative study %K real-world implementation %K mobile health technologies %D 2025 %7 29.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Mobile health (mHealth) is increasingly being used in contemporary health care provision owing to its portability, accessibility, ability to facilitate communication, improved interprofessional collaboration, and benefits for health outcomes. However, there is limited discourse on patient safety in real-world mHealth implementation, especially as care settings extend beyond traditional center-based technology usage to home-based care. Objective: This study aimed to explore health care professionals’ perspectives on the safety aspects of mHealth integration in real-world service provision, focusing on Hong Kong Special Administrative Region (SAR) and Wuhan city in mainland China. In Hong Kong SAR, real-world mHealth care provision is largely managed by the Hospital Authority, which has released various mobile apps for home-based care, such as Stoma Care, Hip Fracture, and HA Go. In contrast, mHealth care provision in Wuhan is institutionally directed, with individual hospitals or departments using consultation apps, WeChat mini-programs, and the WeChat Official Accounts Platform (a subapp within the WeChat ecosystem). Methods: A multicenter qualitative study design was used. A total of 27 participants, including 22 nurses and 5 physicians, from 2 different health care systems were interviewed individually. Thematic analysis was used to analyze the data. Results: The mean age of the participants was 32.19 (SD 3.74) years, and the mean working experience was 8.04 (SD 4.05) years. Most participants were female (20/27, 74%). Nearly half of the participants had a bachelor’s degree (13/27, 48%), some had a master’s degree (9/27, 33%), and few had a diploma degree (3/27, 11%) or a doctoral degree (2/27, 7%). Four themes emerged from the data analysis. Considering the current uncertainties surrounding mHealth implementation, participants emphasized “liability” concerns when discussing patient safety. They emphasized the need for “change management,” which includes appropriate referral processes, adequate resources and funding, informed mHealth usage, and efficient working processes. They cautioned about the risks in providing mHealth information without ensuring understanding, appreciated the current regulations available, and identified additional regulations that should be considered to ensure information security. Conclusions: As health care systems increasingly adopt mHealth solutions globally to enhance both patient care and operational efficiency, it becomes crucial to understand the implications for patient safety in these new care models. Health care professionals recognized the importance of patient safety in making mHealth usage reliable and sustainable. The promotion of mHealth should be accompanied by the standardization of mHealth services with institutional, health care system, and policy-level support. This includes fostering mHealth acceptance among health care professionals to encourage appropriate referrals, accommodate changes, ensure patient comprehension, and proactively identify and address threats to information security. %M 40299494 %R 10.2196/71086 %U https://www.jmir.org/2025/1/e71086 %U https://doi.org/10.2196/71086 %U http://www.ncbi.nlm.nih.gov/pubmed/40299494 %0 Journal Article %@ 2562-7600 %I JMIR Publications %V 8 %N %P e69651 %T Evaluating Nurses’ Perceptions of Documentation in the Electronic Health Record: Multimethod Analysis %A Jacques,Deborah %A Will,John %A Dauterman,Denise %A Zavotsky,Kathleen Evanovich %A Delmore,Barbara %A Doty,Glenn Robert %A O'Brien,Kerry %A Groom,Lisa %K electronic health record %K nurse %K documentation burden %K focus group %K usability %K documentation %D 2025 %7 28.4.2025 %9 %J JMIR Nursing %G English %X Background: Nurses are one of the largest user groups of the electronic health record (EHR) system, relying on its tools to support patient care and nursing workflows. Recent studies suggested that the redesign of nursing documentation may reduce the time spent in the EHR system and improve nurse satisfaction. Objective: We aimed to assess nurses’ perceptions of the redesigned EHR, evaluate the impact of documentation interventions, and identify future improvement needs. Methods: Guided by the American Nursing Informatics Association’s Six Domains of Burden conceptual framework, this multimethod project combined both qualitative and quantitative approaches. Registered nurses across the academic health system were recruited via email invitations to participate in focus group discussions. The focus groups were conducted via a web conference and ranged from 60 to 90 minutes in duration. The focus group discussions were transcribed and analyzed through thematic analysis. The EHR vendor’s time data were used to analyze nurses’ time spent in documentation. Results: In total, 20 registered nurses participated in the focus group discussions, and 17 nurses completed the demographic survey; 88% (15/17) of participants had ≥3 years of EHR experience at the academic health system, and 53% (9/17) self-reported being competent in the EHR system. The following six themes emerged: positive feedback, usability and workflow opportunities, nuisance, training and education, communication, and time spent in the system. EHR vendor time data revealed that the time spent in flowsheets averaged 31.11% per 12-hour shift. Conclusions: Overall, participants reported a positive experience and that the EHR supported patient care. There are opportunities to further reduce redundancies in documentation and implement programs that support continuous learning about EHR and health technology tools. Specific suggestions include optimizing the oral health assessment tool. Analyzing frontline nursing perspectives in the redesign of EHR workflows is imperative for identifying interventions that support nurses’ satisfaction with the EHR. %R 10.2196/69651 %U https://nursing.jmir.org/2025/1/e69651 %U https://doi.org/10.2196/69651 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e65229 %T Vital Sign and Biochemical Data Collection Using Non-contact Photoplethysmography and the Comestai Mobile Health App: Protocol for an Observational Study %A Zuccotti,Gianvincenzo %A Agnelli,Paolo Osvaldo %A Labati,Lucia %A Cordaro,Erika %A Braghieri,Davide %A Balconi,Simone %A Xodo,Marco %A Losurdo,Fabrizio %A Berra,Cesare Celeste Federico %A Pedretti,Roberto Franco Enrico %A Fiorina,Paolo %A De Pasquale,Sergio Maria %A Calcaterra,Valeria %+ Pediatric Department, Buzzi Children’s Hospital, Via Castelvetro n. 32, Milano, 20154, Italy, 39 0263635321, gianvincenzo.zuccotti@unimi.it %K biochemical data %K mHealth %K mobile app %K non-contact photoplethysmography %K detection %K Comestai %K data accuracy %K monitoring %K vital sign measurement %K screening %D 2025 %7 28.4.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Early detection of vital sign changes is key to recognizing patient deterioration promptly, enabling timely interventions and potentially preventing adverse outcomes. Objective: In this study, vital parameters (heart rate, respiratory rate, oxygen saturation, and blood pressure) will be measured using the Comestai app to confirm the accuracy of photoplethysmography methods compared to standard clinical practice devices, analyzing a large and diverse population. In addition, the app will facilitate big data collection to enhance the algorithm’s performance in measuring hemoglobin, glycated hemoglobin, and total cholesterol. Methods: A total of 3000 participants will be consecutively enrolled to achieve the objectives of this study. In all patients, personal data, medical condition, and treatment overview will be recorded. The “by face” method for remote photoplethysmography vital sign data collection involves recording participants’ faces using the front camera of a mobile device (iOS or Android) for approximately 1.5 minutes. Simultaneously, vital signs will be continuously collected for about 1.5 minutes using the reference devices alongside data collected via the Comestai app; biochemical results will also be recorded. The accuracy of the app measurements compared to the reference devices and standard tests will be assessed for all parameters. CIs will be calculated using the bootstrap method. The proposed approach’s effectiveness will be evaluated using various quality criteria, including the mean error, SD, mean absolute error, root mean square error, and mean absolute percentage error. The correlation between measurements obtained using the app and reference devices and standard tests will be evaluated using the Pearson correlation coefficient. Agreement between pairs of measurements (app vs reference devices and standard tests) will be represented using Bland-Altman plots. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and likelihood ratios will be calculated to determine the ability of the new app to accurately measure vital signs. Results: Data collection began in June 2024. As of March 25, 2025, we have recruited 1200 participants. The outcomes of the study are expected at the end of 2025. The analysis plan involves verifying and validating the parameters collected from mobile devices via the app, reference devices, and prescheduled blood tests, along with patient demographic data. Conclusions: Our study will enhance and support the accuracy of data on vital sign detection through PPG, also introducing measurements of biochemical risk indicators. The evaluation of a large population will allow for continuous improvement in the performance and accuracy of artificial intelligence algorithms, reducing errors. Expanding research on mobile health solutions like Comestai can support preventive care by validating their effectiveness as screening tools and guiding future health care technology developments. Trial Registration: ClinicalTrials.gov NCT06427564; https://clinicaltrials.gov/study/NCT06427564 %M 40293779 %R 10.2196/65229 %U https://www.researchprotocols.org/2025/1/e65229 %U https://doi.org/10.2196/65229 %U http://www.ncbi.nlm.nih.gov/pubmed/40293779 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e49363 %T The Benefits of Integrating Electronic Medical Record Systems Between Primary and Specialist Care Institutions: Mixed Methods Cohort Study %A Goh,Kim Huat %A Yeow,Adrian Yong Kwang %A Wang,Le %A Poh,Hermione %A Ng,Hannah Jia Hui %A Tan,Gamaliel %A Wee,Soon Khai %A Lim,Er Luen %A D’Souza,Jared Louis Andre %+ Nanyang Technological University, 91 Nanyang Avenue, Singapore, 639956, Singapore, 65 67904808, akhgoh@ntu.edu.sg %K EMR integration %K primary care %K specialist care %K medical neighborhood %K efficiency %D 2025 %7 22.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: The benefits of a fully integrated electronic medical record (EMR) system across primary and specialist care institutions have yet to be formally established. Integrating the EMR systems between primary and specialist care is the first step in building a medical neighborhood. A medical neighborhood is a set of policies and procedures implemented through integrated systems and processes that support the joint management of patient care across primary care physicians, specialist physicians, and other health care providers. Objective: This study aims to quantify the impacts of integrating the EMR systems of primary and specialist care institutions in the process of developing a medical neighborhood. The impacts are operationalized in both quantitative and qualitative measures, measuring the benefits of such an integration in 3 specific areas, namely, patient diagnosis tracking, patient care management, and patient coordination. Methods: A comprehensive, mixed methods examination was conducted using 3 different data sources (EMR consultation data, clinician survey data, and in-depth interviews). The EMR data consist of patient encounters referred to a specialist clinic from 6 primary care providers before and after integrating the EMR system into the primary and specialist care institutions. We analyzed 25,404 specialist consultation referrals to the specialist clinics by the primary care partners for a 12-month period, during which the integration of the EMR system was conducted. A cohort empirical investigation was used to identify the quantitative impacts of the EMR integration, and a follow-up survey was conducted with the clinicians 18 months post integration. The clinicians’ perceptions of the integration were measured to triangulate the empirical observation from the patient encounters, and the postimplementation perception survey was analyzed to triangulate the empirical investigation of consultation instances of the earlier cohort. Concurrently, a total of 30 interviews were conducted between March 16, 2021, and July 28, 2021, with clinicians and operations staff to gather on-the-ground sentiments engendered by this integration, which further informed our quantitative findings. Results: The integration of EMR systems between primary and specialist care institutions was associated with benefits in patient diagnosis tracking, patient care management, and patient coordination. Specifically, it was found that the integration resulted in a decrease in wait time for specialist appointments of an average of 16.5 days (P<.001). Patients were also subjected to fewer repeated procedures and tests; the number of procedures (P=.006), radiographies (P=.02), and overall bill sizes (P=.004) all decreased by between 4.08% and 39.7%, resulting in reduced health care resource wastage while maintaining similar medical outcomes (P=.37). Conclusions: Our study’s results are among the first instances of empirical evidence to show that the integration and sharing of data between primary and specialist care institutions promote continuity in health care delivery and joint patient management in a medical neighborhood. The findings go beyond the traditional benefits of improved referral communication, as shown in prior literature. %M 40262140 %R 10.2196/49363 %U https://www.jmir.org/2025/1/e49363 %U https://doi.org/10.2196/49363 %U http://www.ncbi.nlm.nih.gov/pubmed/40262140 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e68891 %T Designing Health Care Provider–Centered Emergency Department Interventions: Participatory Design Study %A Seo,Woosuk %A Li,Jiaqi %A Zhang,Zhan %A Zheng,Chuxuan %A Singh,Hardeep %A Pasupathy,Kalyan %A Mahajan,Prashant %A Park,Sun Young %+ School of Information, University of Michigan, 105 S State St, Ann Arbor, MI, 48104, United States, 1 2063109264, seow@umich.edu %K emergency departments %K participatory design %K health care providers %K technology %K interventions %K artificial intelligence %D 2025 %7 21.4.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: In the emergency department (ED), health care providers face extraordinary pressures in delivering accurate diagnoses and care, often working with fragmented or inaccessible patient histories while managing severe time constraints and constant interruptions. These challenges and pressures may lead to potential errors in the ED diagnostic process and risks to patient safety. With advances in technology, interventions have been developed to support ED providers in such pressured settings. However, these interventions may not align with the current practices of ED providers. To better design ED provider–centered interventions, identifying their needs in the diagnostic process is critical. Objective: This study aimed to identify ED providers’ needs in the diagnostic process through participatory design sessions and to propose design guidelines for provider‑centered technological interventions that support decision‑making and reduce errors. Methods: We conducted a participatory design study with ED providers to validate their needs and identify considerations for designing ED provider–centered interventions to improve diagnostic safety. We used 9 technological intervention ideas as storyboards to address the study participants’ needs. We had participants discuss the use cases of each intervention idea to assess their needs during the ED care process and facilitated co-design activities with the participants to improve the technological intervention designs. We audio- and video-recorded the design sessions. We then analyzed session transcripts, field notes, and design sketches. In total, we conducted 6 design sessions with 17 ED frontline providers. Results: Through design sessions with ED providers, we identified 4 key needs in the diagnostic process: information integration, patient prioritization, ED provider-patient communication, and care coordination. We interpreted them as insights for designing technological interventions for ED patients. Hence, we discussed the design implications for technological interventions in four key areas: (1) enhancing ED provider–ED provider communication, (2) enhancing ED provider-patient communication, (3) optimizing the integration of advanced technology, and (4) unleashing the potential of artificial intelligence tools in the ED to improve diagnosis. This work offers evidence-based technology design suggestions for improving diagnostic processes. Conclusions: This study provides unique insights for designing technological interventions to support ED diagnostic processes. By inviting ED providers into the design process, we present unique insights into the diagnostic process and design considerations for designing novel technological interventions that meet ED providers’ needs in the diagnostic process. International Registered Report Identifier (IRRID): RR2-10.2196/55357 %M 40258269 %R 10.2196/68891 %U https://formative.jmir.org/2025/1/e68891 %U https://doi.org/10.2196/68891 %U http://www.ncbi.nlm.nih.gov/pubmed/40258269 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 8 %N %P e60959 %T Quantification of Metamorphopsia Using a Smartphone-Based Hyperacuity Test in Patients With Idiopathic Epiretinal Membranes: Prospective Observational Study %A Amon,Daria %A Leisser,Christoph %A Schlatter,Andreas %A Ruiss,Manuel %A Pilwachs,Caroline %A Bayer,Natascha %A Huemer,Josef %A Findl,Oliver %K mobile health %K smartphone %K telemedicine %K Alleye %K M-chart %K metamorphopsia %K epiretinal membrane %K vitrectomy with membrane peeling %K visual acuity %K home monitoring %K hyperacuity test %K hyperacuity %K surgical intervention %K distorted vision %K vision %K ocular pathology %K ocular %K retinal %K retina %K surgery %K macular degeneration %K tomography %K vitrectomy %K ophthalmology %D 2025 %7 17.4.2025 %9 %J JMIR Perioper Med %G English %X Background: Quality of vision in patients with idiopathic epiretinal membranes (iERMs) is closely linked to distorted vision (metamorphopsia), which is often underestimated in clinical settings. Current surgical decision-making relies heavily on visual acuity and optical coherence tomography findings, which do not adequately reflect the patient’s functional vision or the severity of metamorphopsia. There is a clinical need for tools that can reliably quantify this symptom to improve patient outcomes and streamline care pathways. Objectives: This study is the first to assess the use of a novel smartphone-based hyperacuity test (SHT) in quantifying metamorphopsia before and after surgical intervention for iERMs, comparing it with a conventional printed chart. Methods: This prospective observational study included 27 patients with iERMs with symptomatic metamorphopsia detected on the Amsler grid scheduled for vitrectomy with membrane peeling. The SHT (Alleye, Oculocare Medical Inc) and the horizontal (MH) and vertical (MV) M-chart (Inami & Co, Ltd) tests were performed 3 times before and 3 months after surgery. Pre- and postoperative metamorphopsia scores, changes in distance-corrected visual acuity, optical coherence tomography biomarkers, and subjective perception of metamorphopsia were evaluated. Results: The mean SHT score significantly (r=0.686; P<.001) improved from 55.2 (SD 18.9) before surgery to 63.5 (SD 16.3) after surgery while the improvement of the M-chart scores were insignificant (MH r=0.37, P=.06; MV r=0.18, P=.36). Pre- and postoperative SHT scores showed very weak and insignificant correlations with the MH, MV, and MH+MV scores. Both metamorphopsia tests showed good reliability (intraclass correlation coefficients >0.75). Conclusions: The SHT showed a significant improvement in postoperative metamorphopsia scores, indicating that it could be a valuable tool for quantifying visual distortion in patients with iERMs. While discrepancies with M-chart results were observed, both tests demonstrated good reliability. Clinically, the SHT may offer a practical solution for monitoring metamorphopsia and guiding complex surgical decision-making, particularly in telemedicine settings. Its accessibility could improve patient management, potentially enhancing preoperative triaging and reducing unnecessary visits. Trial Registration: ClinicalTrials.gov NCT05138315; https://clinicaltrials.gov/study/NCT05138315 %R 10.2196/60959 %U https://periop.jmir.org/2025/1/e60959 %U https://doi.org/10.2196/60959 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e70163 %T Locomotive Syndrome Digital Therapeutics Provided via a Smartphone App: Protocol for a Single-Group Trial %A Sonobe,Tatsuru %A Matsumoto,Yoshihiro %+ Department of Orthopaedic Surgery, Fukushima Medical University School of Medicine, 1 Hikarigaoka, Fukushima, 960-1295, Japan, 81 24 547 1276, tatsuru1@fmu.ac.jp %K locomotive syndrome %K locomotion training %K digital therapeutic %K TUG %K 25-Geriatric Locomotive Function Scale %K BREQ-3 %K behavioral change %K support application %D 2025 %7 17.4.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Locomotive syndrome (LS) is a condition in which muscle weakness and reduced motor function due to musculoskeletal disorders cause reduced mobility and physical function. In Japan, musculoskeletal disorders are the most frequent reason for requiring home support or nursing care, and the prevention and amelioration of LS are thus being emphasized. However, it is difficult for older people to make a habit of exercise therapy, which is the mainstay of LS treatment. We investigated whether digital therapy could (1) lead to behavioral change in older people and (2) prevent or improve LS in older people. Objective: We sought to determine whether digital therapeutics (DTx) are useful for the prevention and amelioration of LS in older people, and we assessed the effects of DTx on the participants’ exercise awareness and motor function. Methods: We conducted a multicenter, prospective, longitudinal, nonrandomized, single-group study of Japanese adults aged ≥40 years who were eligible for LS checks. Each participant underwent an 8-week locomotion training (LT) intervention, and their subjective and objective motor abilities and motor awareness were objectively assessed at the following time points: baseline (before the start of the DTx), interim (4 weeks after the start of the DTx), and end (8 weeks after the start of the DTx). We evaluated the participants’ objective motor function using the timed up and go (TUG) test, and we compare the results using a 3-way ANOVA with the TUG test at the 3 evaluation time points as the dependent variable. The results of the 25-question Geriatric Locomotive Function Scale, which is a subjective measure of motor function, and the results of the Behavioural Regulation in Exercise Questionnaire 3, which assesses motor awareness, were also evaluated using an ANOVA in the same way as the TUG test. The significance level was set at .05 / 3 = .0167 after Bonferroni correction. Results: As of April 2025, this study had enrolled 47 participants, and complete data had been gathered from 45 participants for the proposed analysis. Study participation was ongoing as of April 2025. Conclusions: The study cohort will be used as a basis for further observational and intervention studies. This research could lead to more efficient use of medical resources and a reduction in financial and medical burdens on individuals and the economy, and it could support the prevention and amelioration of LS and the establishment of exercise habits among older people. Trial Registration: University Hospital Medical Information Network Clinical Trials Registry UMIN000053922; https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000061550 International Registered Report Identifier (IRRID): DERR1-10.2196/70163 %M 40246298 %R 10.2196/70163 %U https://www.researchprotocols.org/2025/1/e70163 %U https://doi.org/10.2196/70163 %U http://www.ncbi.nlm.nih.gov/pubmed/40246298 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e66366 %T Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study %A Kwun,Ju-Seung %A Ahn,Houng-Beom %A Kang,Si-Hyuck %A Yoo,Sooyoung %A Kim,Seok %A Song,Wongeun %A Hyun,Junho %A Oh,Ji Seon %A Baek,Gakyoung %A Suh,Jung-Won %+ Cardiovascular Center, Department of Internal Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Gyeonggi-do, Seongnam-si, 13620, Republic of Korea, 82 01076615931, suhjw1@gmail.com %K perioperative risk evaluation %K noncardiac surgery %K prediction models %K machine learning %K common data model %K ML %K predictive modeling %K cerebrovascular %K electronic health records %K EHR %K clinical practice %K risk %K noncardiac surgeries %K perioperative %D 2025 %7 9.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Considering that most patients with low or no significant risk factors can safely undergo noncardiac surgery without additional cardiac evaluation, and given the excessive evaluations often performed in patients undergoing intermediate or higher risk noncardiac surgeries, practical preoperative risk assessment tools are essential to reduce unnecessary delays for urgent outpatient services and manage medical costs more efficiently. Objective: This study aimed to use the Observational Medical Outcomes Partnership Common Data Model to develop a predictive model by applying machine learning algorithms that can effectively predict major adverse cardiac and cerebrovascular events (MACCE) in patients undergoing noncardiac surgery. Methods: This retrospective observational network study collected data by converting electronic health records into a standardized Observational Medical Outcomes Partnership Common Data Model format. The study was conducted in 2 tertiary hospitals. Data included demographic information, diagnoses, laboratory results, medications, surgical types, and clinical outcomes. A total of 46,225 patients were recruited from Seoul National University Bundang Hospital and 396,424 from Asan Medical Center. We selected patients aged 65 years and older undergoing noncardiac surgeries, excluding cardiac or emergency surgeries, and those with less than 30 days of observation. Using these observational health care data, we developed machine learning–based prediction models using the observational health data sciences and informatics open-source patient-level prediction package in R (version 4.1.0; R Foundation for Statistical Computing). A total of 5 machine learning algorithms, including random forest, were developed and validated internally and externally, with performance assessed through the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve, and calibration plots. Results: All machine learning prediction models surpassed the Revised Cardiac Risk Index in MACCE prediction performance (AUROC=0.704). Random forest showed the best results, achieving AUROC values of 0.897 (95% CI 0.883-0.911) internally and 0.817 (95% CI 0.815-0.819) externally, with an area under the precision-recall curve of 0.095. Among 46,225 patients of the Seoul National University Bundang Hospital, MACCE occurred in 4.9% (2256/46,225), including myocardial infarction (907/46,225, 2%) and stroke (799/46,225, 1.7%), while in-hospital mortality was 0.9% (419/46,225). For Asan Medical Center, 6.3% (24,861/396,424) of patients experienced MACCE, with 1.5% (6017/396,424) stroke and 3% (11,875/396,424) in-hospital mortality. Furthermore, the significance of predictors linked to previous diagnoses and laboratory measurements underscored their critical role in effectively predicting perioperative risk. Conclusions: Our prediction models outperformed the widely used Revised Cardiac Risk Index in predicting MACCE within 30 days after noncardiac surgery, demonstrating superior calibration and generalizability across institutions. Its use can optimize preoperative evaluations, minimize unnecessary testing, and streamline perioperative care, significantly improving patient outcomes and resource use. We anticipate that applying this model to actual electronic health records will benefit clinical practice. %M 40203300 %R 10.2196/66366 %U https://www.jmir.org/2025/1/e66366 %U https://doi.org/10.2196/66366 %U http://www.ncbi.nlm.nih.gov/pubmed/40203300 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e57782 %T Impact of Electronic Transition and Prefilled Templates on Drug Prescription Compliance: Retrospective Study %A Lambert,Aurélien %A Hombourger,Benoit %A Salleron,Julia %A Chergui,Fadila %A Vallance,Catherine %A Nicolas,Nadège %A Moussouni,Marie %A Cherif,Lounisse %A Chenot,Emile %A Gavoille,Céline %A Massard,Vincent %+ , Institut de Cancérologie de Lorraine, 6 avenue de bourgogne, Vandoeuvre-lès-Nancy, 54500, France, 33 383598400, a.lambert@nancy.unicancer.fr %K drug prescription %K electronic prescription %K handwriting %K medical oncology %K ambulatory care %D 2025 %7 9.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: The transition from traditional handwritten prescriptions to electronic prescribing systems represents a significant advancement, with the potential to enhance treatment efficacy, patient safety, and professional communication. Objective: This study aimed to examine the impact of this transition within a medical oncology service, assessing the compliance of electronic prescriptions with established good practice standards and exploring the associated risks. Methods: In this retrospective analysis, we compared handwritten prescriptions from the pre-electronic era (January to May 2018) with electronic prescriptions (January to May 2021) following the implementation of the electronic prescribing system PandaLab Pro (PandaLab SAS). The inclusion criteria focused on outpatient oncology treatments, with a clear set of exclusion parameters to ensure a focused study scope. We defined good compliance as the written mention of the evaluated terms. The compliance rates were then compared using a chi-square test. Results: Our findings, based on a sample size of 260 prescriptions (randomized among 30,526 archived prescriptions), indicate a substantial improvement in electronic prescriptions’ compliance with prescribers and patient details, treatment accuracy, and overall adherence to regulatory standards. Notably, electronic formats achieved a remarkable 80.8% accuracy rate in compliance with safety criteria compared with 8.5% for handwritten prescriptions (P<.001). The use of prefilled prescriptions significantly increased compliance from a safety perspective (56% vs 96.2%; P<.001) compared with electronic prescriptions from scratch. Conclusions: The analysis further underscores the advantages of prefilled electronic prescription templates, which significantly improved compliance rates compared with manually filled electronic and handwritten prescriptions. Furthermore, the study revealed a marked shift in prescribing behaviors, with electronic prescriptions tending to be more concise yet more numerous, suggesting an impact on medication management and patient adherence, which warrants further investigation. The study supports the transition to electronic prescribing systems in oncology, highlighting enhanced traceability, compliance with health authority standards, and patient safety. The implementation of prefilled templates supported by pharmacists has emerged as a pivotal factor in this improved process. While acknowledging certain limitations, such as the nonquantitative assessment of time savings and acceptability, this research advocates for the widespread adoption of electronic prescriptions and serves as a benchmark for future e-prescription initiatives in France. %M 40202779 %R 10.2196/57782 %U https://www.jmir.org/2025/1/e57782 %U https://doi.org/10.2196/57782 %U http://www.ncbi.nlm.nih.gov/pubmed/40202779 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e68454 %T Clinical Benefits and Risks of Antiamyloid Antibodies in Sporadic Alzheimer Disease: Systematic Review and Network Meta-Analysis With a Web Application %A Jeremic,Danko %A Navarro-Lopez,Juan D %A Jimenez-Diaz,Lydia %+ Neurophysiology & Behavior Lab, Institute of Biomedicine (IB-UCLM) and Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Faculty of Medicine of Ciudad Real, University of Castilla-La Mancha, Camino Moledores s/n, Ciudad Real, 13071, Spain, 34 926295300, lydia.jimenez@uclm.es %K Alzheimer disease %K antibodies %K donanemab %K aducanumab %K lecanemab %D 2025 %7 7.4.2025 %9 Review %J J Med Internet Res %G English %X Background: Despite the increasing approval of antiamyloid antibodies for Alzheimer disease (AD), their clinical relevance and risk-benefit profile remain uncertain. The heterogeneity of AD and the limited availability of long-term clinical data make it difficult to establish a clear rationale for selecting one treatment over another. Objective: The aim of this work was to assess and compare the efficacy and safety of antiamyloid antibodies through an interactive online meta-analytic approach by performing conventional pair-wise meta-analyses and frequentist and Bayesian network meta-analyses of phase II and III clinical trial results. To achieve this, we developed AlzMeta.app 2.0, a freely accessible web application that enables researchers and clinicians to evaluate the relative and absolute risks and benefits of these therapies in real time, incorporating different prior choices and assumptions of baseline risks of disease progression and adverse events. Methods: We adhered to PRISMA-NMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for reporting of systematic reviews with network meta-analysis) and GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) guidelines for reporting and rating the certainty of evidence. Clinical trial reports (until September 30, 2024) were retrieved from PubMed, Google Scholar, and clinical trial databases (including ClinicalTrials.gov). Studies with <20 sporadic AD patients and a modified Jadad score <3 were excluded. Risk of bias was assessed with the RoB-2 tool. Relative risks and benefits have been expressed as risk ratios and standardized mean differences, with confidence, credible, and prediction intervals calculated for all outcomes. For significant results, the intervention effects were ranked in frequentist and Bayesian frameworks, and their clinical relevance was determined by the absolute risk per 1000 people and number needed to treat (NNT) for a wide range of control responses. Results: Among 7 treatments tested in 21,236 patients (26 studies with low risk of bias or with some concerns), donanemab was the best-ranked treatment on cognitive and functional measures, and it was almost 2 times more effective than aducanumab and lecanemab and significantly more beneficial than other treatments on the global (cognitive and functional) Clinical Dementia Rating Scale-Sum of Boxes (NNT=10, 95% CI 8-16). Special caution is required regarding cerebral edema and microbleeding due to the clinically relevant risks of edema for donanemab (NNT=8, 95% CI 5-16), aducanumab (NNT=10, 95% CI 6-17), and lecanemab (NNT=14, 95% CI 7-31), which may outweigh the benefits. Conclusions: Our results showed that donanemab is more effective and has a safety profile similar to aducanumab and lecanemab, highlighting the need for treatment options with improved safety. Potential bias may have been introduced in the included trials due to unblinding caused by frequent cerebral edema and microbleeds, as well as the impact of the COVID-19 pandemic. %M 40194268 %R 10.2196/68454 %U https://www.jmir.org/2025/1/e68454 %U https://doi.org/10.2196/68454 %U http://www.ncbi.nlm.nih.gov/pubmed/40194268 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e62957 %T Clinicians’ Perceptions and Potential Applications of Robotics for Task Automation in Critical Care: Qualitative Study %A Song,Jiafeng %A Sridhar,Rishika Iytha %A Rogers,Darlene Marie %A Hiddleson,Cheryl %A Davis,Carolyn %A Holden,Tina Lynn %A Ramsey-Haynes,Shanna %A Reif,Lisa %A Swann,Julie %A Jabaley,Craig S %A Gullatte,Mary %A Kamaleswaran,Rishikesan %+ , Department of Biomedical Engineering, Duke University, 2301 Erwin Road, Durham, NC, 27710, United States, 1 7163523686, sjfsjf2010@gmail.com %K robotics %K intensive care units %K critical care %K health care technology %K qualitative study %D 2025 %7 28.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Interest in integrating robotics within intensive care units (ICUs) has been propelled by technological advancements, workforce challenges, and heightened clinical demands, including during the COVID-19 pandemic. The integration of robotics in ICUs could potentially enhance patient care and operational efficiency amid existing challenges faced by health care professionals, including high workload and decision-making complexities. Objective: This qualitative study aimed to explore ICU clinicians’ perceptions of robotic technology and to identify the types of tasks that might benefit from robotic assistance. We focused on the degree of acceptance, perceived challenges, and potential applications for improving patient care in 5 Southeastern US hospitals between January and August 2023. Methods: A qualitative study through semistructured interviews and questionnaires was conducted with 15 ICU clinicians (7 nurses, 6 physicians, and 2 advanced practice providers) from 5 hospitals in the Southeast United States. Directed content analysis was used to categorize and interpret participants’ statements, with statistical tests used to examine any role-based differences in how they viewed robotic integration. Results: Among the 15 participants, 73% (11/15) were female, with an average of 6.4 (SD 6.3) years of ICU experience. We identified 78 distinct tasks potentially suitable for robotic assistance, of which 50 (64%) involved direct patient care (eg, repositioning patients and assisting with simple procedures), 19 (24%) concerned indirect patient care (eg, delivering supplies and cleaning), 6 (8%) addressed administrative tasks (eg, answering call lights), and 3 (4%) were classified as mixed direct and indirect (eg, sitting with a patient to keep them calm). Most participants supported the automation of routine, noncritical tasks (eg, responding to nurse calls and measuring glucose levels), viewing this strategy as a way to alleviate workload and enhance efficiency. Conversely, high-complexity tasks requiring nuanced clinical judgment (eg, ventilator settings) were deemed unsuitable for full automation. Statistical analysis revealed no significant difference in how nurses, physicians, and advanced practice providers perceived these tasks (P=.22). Conclusions: Our findings indicate a significant opportunity to use robotic systems to perform noncomplex tasks in ICUs, thereby potentially improving efficiency and reducing staff burden. Clinicians largely view robots as supportive tools rather than substitutes for human expertise. However, concerns persist regarding privacy, patient safety, and the loss of human touch, particularly for tasks requiring high-level clinical decision-making. Future research should involve broader, more diverse clinician samples and investigate the long-term impact of robotic assistance on patient outcomes while also incorporating patient perspectives to ensure ethical, patient-centered adoption of robotic technology. %M 40153785 %R 10.2196/62957 %U https://www.jmir.org/2025/1/e62957 %U https://doi.org/10.2196/62957 %U http://www.ncbi.nlm.nih.gov/pubmed/40153785 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e63937 %T Explainable AI for Intraoperative Motor-Evoked Potential Muscle Classification in Neurosurgery: Bicentric Retrospective Study %A Parduzi,Qendresa %A Wermelinger,Jonathan %A Koller,Simon Domingo %A Sariyar,Murat %A Schneider,Ulf %A Raabe,Andreas %A Seidel,Kathleen %+ , Department of Neurosurgery, Lucerne Cantonal Hospital, Spitalstrasse, Lucerne, 6000, Switzerland, 41 412056631, qendresa.parduzi@students.unibe.ch %K intraoperative neuromonitoring %K motor evoked potential %K artificial intelligence %K machine learning %K deep learning %K random forest %K convolutional neural network %K explainability %K medical informatics %K personalized medicine %K neurophysiological %K monitoring %K orthopedic %K motor %K neurosurgery %D 2025 %7 24.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Intraoperative neurophysiological monitoring (IONM) guides the surgeon in ensuring motor pathway integrity during high-risk neurosurgical and orthopedic procedures. Although motor-evoked potentials (MEPs) are valuable for predicting motor outcomes, the key features of predictive signals are not well understood, and standardized warning criteria are lacking. Developing a muscle identification prediction model could increase patient safety while allowing the exploration of relevant features for the task. Objective: The aim of this study is to expand the development of machine learning (ML) methods for muscle classification and evaluate them in a bicentric setup. Further, we aim to identify key features of MEP signals that contribute to accurate muscle classification using explainable artificial intelligence (XAI) techniques. Methods: This study used ML and deep learning models, specifically random forest (RF) classifiers and convolutional neural networks (CNNs), to classify MEP signals from routine supratentorial neurosurgical procedures from two medical centers according to muscle identity of four muscles (extensor digitorum, abductor pollicis brevis, tibialis anterior, and abductor hallucis). The algorithms were trained and validated on a total of 36,992 MEPs from 151 surgeries in one center, and they were tested on 24,298 MEPs from 58 surgeries from the other center. Depending on the algorithm, time-series, feature-engineered, and time-frequency representations of the MEP data were used. XAI techniques, specifically Shapley Additive Explanation (SHAP) values and gradient class activation maps (Grad-CAM), were implemented to identify important signal features. Results: High classification accuracy was achieved with the RF classifier, reaching 87.9% accuracy on the validation set and 80% accuracy on the test set. The 1D- and 2D-CNNs demonstrated comparably strong performance. Our XAI findings indicate that frequency components and peak latencies are crucial for accurate MEP classification, providing insights that could inform intraoperative warning criteria. Conclusions: This study demonstrates the effectiveness of ML techniques and the importance of XAI in enhancing trust in and reliability of artificial intelligence–driven IONM applications. Further, it may help to identify new intrinsic features of MEP signals so far overlooked in conventional warning criteria. By reducing the risk of muscle mislabeling and by providing the basis for possible new warning criteria, this study may help to increase patient safety during surgical procedures. %M 40127441 %R 10.2196/63937 %U https://www.jmir.org/2025/1/e63937 %U https://doi.org/10.2196/63937 %U http://www.ncbi.nlm.nih.gov/pubmed/40127441 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e60019 %T Application of Internet Hospitals in the Disease Management of Patients With Ulcerative Colitis: Retrospective Study %A Yu,Tianzhi %A Li,Wanyu %A Liu,Yingchun %A Jin,Chunjie %A Wang,Zimin %A Cao,Hailong %+ Department of Gastroenterology, National Key Clinical Specialty, Tianjin Medical University General Hospital, 154 Anshan Road in Heping District, Tianjin, 300052, China, 86 +86 022 6036155, caohailong@tmu.edu.cn %K inflammatory bowel disease %K ulcerative colitis %K intelligent diagnosis and treatment service %K internet hospital %K chronic disease management %D 2025 %7 18.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Ulcerative colitis (UC) is a chronic disease characterized by frequent relapses, requiring long-term management and consuming substantial medical and social resources. Effective management of UC remains challenging due to the need for sustainable remission strategies, continuity of care, and access to medical services. Intelligent diagnosis refers to the use of artificial intelligence–driven algorithms to analyze patient-reported symptoms, generate diagnostic probabilities, and provide treatment recommendations through interactive tools. This approach could potentially function as a method for UC management. Objective: This study aimed to analyze the diagnosis and treatment data of UC from both physical hospitals and internet hospitals, highlighting the potential benefits of the intelligent diagnosis and treatment service model offered by internet hospitals. Methods: We collected data on the visits of patients with UC to the Department of Gastroenterology at Tianjin Medical University General Hospital. A total of 852 patients with UC were included between July 1, 2020, and June 31, 2023. Statistical methods, including chi-square tests for categorical variables, t tests for continuous variables, and rank-sum tests for visit numbers, were used to evaluate the medical preferences and expenses of patients with UC. Results: We found that internet hospitals and physical hospitals presented different medical service models due to the different distribution of medical needs and patient groups. Patients who chose internet hospitals focused on disease consultation and prescription medication (3295/3528, 93.40%). Patients’ medical preferences gradually shifted to web-based services provided by internet hospitals. Over time, 58.57% (270/461) of patients chose either web-based services or a combination of web-based and offline services for UC diagnosis and treatment. The number of visits in the combination of web-based and offline service modes was the highest (mean 13.83, SD 11.07), and younger patients were inclined to visit internet hospitals (49.66%>34.71%). In addition, compared with physical hospitals, there was no difference in testing fees and examination fees for patients with UC in internet hospitals, but medicine fees were lower. Conclusions: The intelligent diagnosis and treatment model provided by internet hospitals demonstrates the potential benefits in managing UC, including feasibility, accessibility, convenience, and economics. %M 40101745 %R 10.2196/60019 %U https://www.jmir.org/2025/1/e60019 %U https://doi.org/10.2196/60019 %U http://www.ncbi.nlm.nih.gov/pubmed/40101745 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e63095 %T Digital Translation Platform (Translatly) to Overcome Communication Barriers in Clinical Care: Pilot Study %A Olsavszky,Victor %A Bazari,Mutaz %A Dai,Taieb Ben %A Olsavszky,Ana %A Finkelmeier,Fabian %A Friedrich-Rust,Mireen %A Zeuzem,Stefan %A Herrmann,Eva %A Leipe,Jan %A Michael,Florian Alexander %A Westernhagen,Hans von %A Ballo,Olivier %+ Department of Dermatology, Venereology and Allergy, University Medical Center and Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, Mannheim, 68167, Germany, 49 621 383 2280, victor.olsavszky@medma.uni-heidelberg.de %K language barriers %K health care communication %K medical app %K real-time translation %K medical translation %D 2025 %7 14.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Language barriers in health care can lead to misdiagnosis, inappropriate treatment, and increased medical errors. Efforts to mitigate these include using interpreters and translation tools, but these measures often fall short, particularly when cultural nuances are overlooked. Consequently, medical professionals may have to rely on their staff or patients’ relatives for interpretation, compromising the quality of care. Objective: This formative pilot study aims to assess the feasibility of Translatly, a digital translation platform, in clinical practice. Specifically, the study focuses on evaluating (1) how health care professionals overcome language barriers and their acceptance of an on-demand video telephony platform, (2) the feasibility of the platform during medical consultations, and (3) identifying potential challenges for future development. Methods: The study included ethnographic interviews with health care professionals and an observational pilot to assess the use of the Translatly platform in clinical practice. Translatly was developed to make real-time translation easy and accessible on both Android and iOS devices. The system’s backend architecture uses Java-based services hosted on DigitalOcean. The app securely exchanges data between mobile devices and servers, with user information and call records stored in a MySQL database. An admin panel helps manage the system, and Firebase integration enables fast push notifications to ensure that health care professionals can connect with translators whenever they need to. The platform was piloted in a German university hospital with 170 volunteer nonprofessional translators, mainly medical students, supporting translation in over 20 languages, including Farsi, Dari, and Arabic. Results: Ethnographic research conducted by interviewing health care professionals in Frankfurt am Main and other German cities revealed that current practices for overcoming language barriers often rely on family members or digital tools such as Google Translate, raising concerns about accuracy and emotional distress. Respondents preferred an on-demand translation service staffed by medically experienced translators, such as medical students, who understand medical terminology and can empathize with patients. The observational pilot study recorded 39 requests for translation services, 16 (41%) of which were successfully completed. The translations covered 6 different languages and were carried out by a team of 10 translators. Most requests came from departments such as infectious diseases (5/16, 31%) and emergency (4/16, 25%). Challenges were identified around translator availability, with 23 (59%) total requests (N=39) going unanswered, which was further evidenced by user feedback. Conclusions: This pilot study demonstrates the feasibility of the Translatly platform in real-world health care settings. It shows the potential to improve communication and patient outcomes by addressing language barriers. Despite its potential, challenges such as translator availability highlight the need for further development. %M 39451122 %R 10.2196/63095 %U https://formative.jmir.org/2025/1/e63095 %U https://doi.org/10.2196/63095 %U http://www.ncbi.nlm.nih.gov/pubmed/39451122 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e58334 %T The Measurement of Vital Signs in Pediatric Patients by Lifelight Software in Comparison to the Standard of Care: Protocol for the VISION-Junior Observational Study %A Misra,Gauri %A Wegerif,Simon %A Fairlie,Louise %A Kapoor,Melissa %A Fok,James %A Salt,Gemma %A Halbert,Jay %A Maconochie,Ian %A Mullen,Niall %+ , Mind over Matter Medtech Ltd, Kemp House, 160 City Road, London, EC1V 2NX, United Kingdom, 44 7881927063, melissa@mind-medtech.com %K vital signs %K remote photoplethysmography %K pediatric health assessment %K pediatric health monitoring %K pediatric %K infant %K infants %K infancy %K child %K children %K Lifelight %K software %K app %K observational study %K study protocol %K clinical deterioration %K COVID-19 %K SARS-CoV-2 %K pandemic %K telemedicine %K medical device %K photoplethysmography %K eHealth %K mobile health %K mHealth %D 2025 %7 14.3.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Measuring vital signs (VS) is important in potentially unwell children, as a change in VS may indicate a more serious infection than is clinically apparent or herald clinical deterioration. However, currently available methods are not suitable for regular measurement of VS in the home or community setting, and adherence can be poor. The COVID-19 pandemic highlighted a need for the contactless measurement of VS by nonclinical personnel, reinforced by the growing use of telemedicine. The Lifelight app is being developed as a medical device for the contactless measurement of VS using remote photoplethysmography via the camera on smart devices. The VISION-D (Measurement of Vital Signs by Lifelight Software in Comparison to the Standard of Care—Development) and -V (Validation) studies demonstrated the accuracy of the app compared with standard of care (SOC) measurement of blood pressure, pulse rate (PR), and respiratory rate (RR) in adults, supporting certification of Lifelight as a class I Conformité Européenne medical device. Objective: To support the development of the Lifelight app for pediatric patients, the VISION-Junior study is collecting high-quality data that will be used to develop algorithms for the measurement of VS (PR, RR, and oxygen saturation) in pediatric patients. The accuracy of the app will be assessed against SOC measurements made simultaneously with app measurements. Methods: The study is recruiting pediatric patients (younger than 18 years of age) attending the Sunderland Royal Hospital pediatric emergency department of the South Tyneside and Sunderland National Health Service Foundation Trust. High-resolution videos of the face (and torso in children younger than 5 years of age) and audio recordings (to explore the value of crying, wheezing, coughing, and other sounds in predicting illness) are made using the Lifelight Data Collect app. VS are measured simultaneously using SOC methods (finger clip sensor for PR and oxygen saturation; manual counting of RR). Feedback from patients, parents, carers, and nurses who use Lifelight is collected via questionnaires. Anticipated recruitment is 500 participants, with subtargets for age, sex, and skin tone distribution (Fitzpatrick 6-point scale). Early data will be used to refine the algorithms. A separate dataset will be retained to test the performance of the app against predefined targets. Results: The study started on June 12, 2023, and reached its recruitment target (n=532) in April 2024 after extending the deadline. Algorithm refinement is in progress, after which the performance of Lifelight will be compared with the SOC measurement of VS. The analyses are expected to be completed by mid-August 2024. Conclusions: Data collected in this study will be used to develop and assess the accuracy of the app for the measurement of VS in pediatric patients of all ages. Trial Registration: ClinicalTrials.gov NCT05850013; https://clinicaltrials.gov/study/NCT05850013 International Registered Report Identifier (IRRID): DERR1-10.2196/58334 %M 40085833 %R 10.2196/58334 %U https://www.researchprotocols.org/2025/1/e58334 %U https://doi.org/10.2196/58334 %U http://www.ncbi.nlm.nih.gov/pubmed/40085833 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e66695 %T Digital Assessment of Cognitive Health in Outpatient Primary Care: Usability Study %A Doerr,Adam J %A Orwig,Taylor A %A McNulty,Matthew %A Sison,Stephanie Denise M %A Paquette,David R %A Leung,Robert %A Ding,Huitong %A Erban,Stephen B %A Weinstein,Bruce R %A Guilarte-Walker,Yurima %A Zai,Adrian H %A Walkey,Allan J %A Soni,Apurv %A McManus,David D %A Lin,Honghuang %+ Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, S6-755, 55 Lake Avenue North, Worcester, MA, 01655, United States, 1 7744554881, honghuang.lin@umassmed.edu %K cognitive assessment %K primary care %K digital %K cognitive impairment %K digital assessment %K assessment %K cognitive health %K cognition %K primary care %K cognitive evaluation %K Core Cognitive Evaluation %K CCE %K cohort %K impairment %K cognitive %K outpatient %D 2025 %7 12.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Screening for cognitive impairment in primary care is important, yet primary care physicians (PCPs) report conducting routine cognitive assessments for less than half of patients older than 60 years of age. Linus Health’s Core Cognitive Evaluation (CCE), a tablet-based digital cognitive assessment, has been used for the detection of cognitive impairment, but its application in primary care is not yet studied. Objective: This study aimed to explore the integration of CCE implementation in a primary care setting. Methods: A cohort of participants was recruited from the upcoming schedules of participating PCPs at UMass Memorial Medical Center. Eligibility criteria included individuals aged ≥65 years; ability to read, write, and speak in English or Spanish; no previous diagnosis of cognitive impairment; and no known untreated hearing or vision impairment. Research coordinators collected consent from participants and facilitated the screening process. PCPs reviewed reports in real time, immediately before the scheduled visits, and shared results at their discretion. A report was uploaded to each participant’s REDCap (Research Electronic Data Capture; Vanderbilt University) record and linked to the encounter in the electronic health record. Feedback from patients and their caregivers (if applicable) was collected by a tablet-based survey in the clinic before and after screening. Participating PCPs were interviewed following the completion of the study. Results: The screened cohort included 150 patients with a mean age of 74 (SD 7) years, of whom 65% (97/150) were female. The CCE identified 40 patients as borderline and 7 as positive for cognitive impairment. A total of 84 orders were placed for select laboratory tests or referrals to neurology and neuropsychology within 20 days of CCE administration. Before the assessment, 95% (143/150) of patients and all 15 caregivers expressed a desire to know if their or their loved one’s brain health was declining. All except one patient also completed the postassessment survey. Among them, 96% (143/149) of patients reported finding the CCE easy to complete, and 70% (105/149) felt that the experience was beneficial. In addition, 87% (130/149) of patients agreed or strongly agreed that they wanted to know their CCE results. Among the 7 participating PCPs, 6 stated that the CCE results influenced their patient care management, and all 7 indicated they would continue using the CCE if it were made available after the study. Conclusions: We explored the integration of the CCE into primary care visits, which showed minimal disruption to the practice workflow. Future studies will be warranted to further validate the implementation of digital cognitive impairment screening tools within primary care settings in the real world. %M 40073397 %R 10.2196/66695 %U https://formative.jmir.org/2025/1/e66695 %U https://doi.org/10.2196/66695 %U http://www.ncbi.nlm.nih.gov/pubmed/40073397 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e66347 %T Impact on Patient Outcomes of Continuous Vital Sign Monitoring on Medical Wards: Propensity-Matched Analysis %A Rowland,Bradley %A Saha,Amit %A Motamedi,Vida %A Bundy,Richa %A Winsor,Scott %A McNavish,Daniel %A Lippert,William %A Khanna,Ashish K %+ Department of Internal Medicine, Section of Hospital Medicine, Wake Forest University School of Medicine, 1 Medical Center Boulevard, Winston-Salem, NC, 27157, United States, 1 336 716 9218, barowlan@wakehealth.edu %K clinical %K continuous %K monitoring %K outcomes %K medical ward %K wireless %K wireless monitoring %K vital sign %K ward %K patient outcome %K hospital ward %K clinical outcome %K contemporaneous control %K contemporaneous %K teenager %K young adult %K adult %K monitoring device %K wireless device %K wearable %K patient monitoring %D 2025 %7 11.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Continuous and wireless vital sign (VS) monitoring on hospital wards is superior to intermittent VS monitoring at detecting VS abnormalities; however, the impact on clinical outcomes remains to be confirmed. A recent propensity-matched study of primary surgical patients found decreased odds of intensive care unit (ICU) admission and mortality in patients receiving continuous monitoring. Primary surgical patients are inherently different from their medical counterparts who typically have high morbidity, including frailty. Continuous monitoring research has been limited in primary medical patients. Objective: This study aims to evaluate the clinical outcomes of primary medical patients who received either continuous or, as a contemporaneous control, intermittent vital monitoring as the standard of care using propensity matching. Methods: Propensity-matched analysis of a population-based sample of 7971 patients admitted to the medical wards between January 2018 and December 2019 at a single, tertiary United States medical center. The continuous monitoring device measures oxygen saturation, heart rate, respiratory rate, continuous noninvasive blood pressure, and either 3-lead or 5-lead electrocardiogram. Patients received either 12 hours or more of continuous and wireless VS monitoring (n=1450) or intermittent VS monitoring (n=6521). The primary outcome was the odds of a composite of in-hospital mortality or ICU transfer during hospitalization. Secondary outcomes were the odds of individual components of the primary outcome, as well as heart failure (HF), myocardial infarction (MI), acute kidney injury (AKI), and rapid response team (RRT) activations. Results: Those who received intermittent VS monitoring had greater odds of a composite of in-hospital mortality or ICU admission (odds ratio [OR] 2.79, 95% CI 1.89-4.25; P<.001) compared with those who had continuous and wireless VS monitoring. The odds of HF (OR 1.03, 95% CI 0.83-1.28; P=.77), MI (OR 1.58, 95% CI 0.77-3.47; P=.23), AKI (OR 0.74, 95% CI 0.62-1.02; P=.06), and RRT activation (OR 0.94, 95% CI 0.75-1.19; P=.62) were similar in both groups. Conclusions: In this propensity-matched study, medical ward patients who received standard of care intermittent VS monitoring were at nearly 3 times greater odds of transfer to the ICU or death compared with those who received continuous VS monitoring. Our study was primarily limited by the inability to match patients on admission diagnosis due to limitations in electronic health record data. Other limitations included the number of and reasons for false alarms, which can be challenging with continuous monitoring strategies. Given the limitations of this work, these observations need to be confirmed with prospective interventional trials. %M 40068153 %R 10.2196/66347 %U https://www.jmir.org/2025/1/e66347 %U https://doi.org/10.2196/66347 %U http://www.ncbi.nlm.nih.gov/pubmed/40068153 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e65685 %T The Effectiveness of Digital Animation–Based Multistage Education for Patients With Atrial Fibrillation Catheter Ablation: Randomized Clinical Trial %A Shi,Xiaoyu %A Wang,Yijun %A Wang,Yuhong %A Wang,Jun %A Peng,Chen %A Cheng,Siyi %A Song,Lingpeng %A Li,Rui %A Guo,Fuding %A Li,Zeyan %A Duan,Shoupeng %A Yang,Xiaomeng %A Zhou,Liping %A Jiang,Hong %A Yu,Lilei %+ Department of Cardiology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuhan, 430060, China, 86 02788041911, lileiyu@whu.edu.cn %K animation education %K digital health care %K atrial fibrillation %K catheter ablation %K video %K mHealth %K digital care %K digital health %K digital animation %K randomized clinical trial %K RCT %K digital education %K outpatient %K AFCA %K atrial fibrillation catheter ablation %K therapeutic %K cardiac arrhythmia %K Asian %K animations %K comics %D 2025 %7 11.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Digital education for outpatient patients with atrial fibrillation (AF) has gradually increased. However, research on digital education for patients undergoing atrial fibrillation catheter ablation (AFCA) is limited. Objective: This study aimed to develop a novel digital animation-based multistage education system and evaluate its quality-of-life benefits for patients with AFCA. Methods: This randomized controlled clinical trial included 208 patients with AF who underwent catheter ablation in the Department of Cardiology at Renmin Hospital of Wuhan University between January 2022 and August 2023. The patients were randomly assigned to the digital animation intervention (n=104) and standard treatment (n=104) groups. The primary outcome was the difference in the quality of life of patients with atrial fibrillation (AF-QoL-18) scores at 3 months. Secondary outcomes included differences in scores on the 5-item Medication Adherence Report Scale (MARS-5), Self-rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS) at 3 months. Results: In the digital animation intervention group, the AF-QoL-18 score increased from 38.02 (SD 6.52) to 47.77 (SD 5.74), the MARS-5 score increased from 17.04 (SD 3.03) to 20.13 (SD 2.12), the SAS score decreased from 52.82 (SD 8.08) to 45.39 (SD 6.13), and the SDS score decreased from 54.12 (SD 6.13) to 45.47 (SD 5.94), 3 months post discharge from the hospital. In the conventional treatment group, the AF-QoL-18 score increased from 36.97 (SD 7.00) to 45.31 (SD 5.71), the MARS-5 score increased from 17.14 (SD 3.01) to 18.47 (SD 2.79), the SAS score decreased from 51.83 (SD 7.74) to 47.31 (SD 5.87), and the SDS score decreased from 52.78 (SD 5.21) to 45.37 (SD 6.18). The mean difference in AF-QoL-18 score change between the 2 groups was 1.41 (95% CI 2.42-0.40, P=.006) at 3 months. The mean difference in MARS-5 score change was 1.76 (95% CI 2.42-1.10, P<.001). The mean difference in SAS score was –2.91 (95% CI –3.88 to –1.95, P<.001). Additionally, the mean difference in SDS score was –1.23 (95% CI –0.02 to –2.44, P=.047). Conclusions: Our study introduces a novel digital animation educational approach that provides multidimensional, easily understandable, and multistage education for patients with AF undergoing catheter ablation. This educational model effectively improves postoperative anxiety, depression, medication adherence, and quality of life in patients at 3 months post discharge. Trial Registration: Chinese Clinical Trial Registry ChiCTR2400081673; https://www.chictr.org.cn/showproj.html?proj=201059 %M 40067344 %R 10.2196/65685 %U https://www.jmir.org/2025/1/e65685 %U https://doi.org/10.2196/65685 %U http://www.ncbi.nlm.nih.gov/pubmed/40067344 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e64239 %T Evaluation of the MyFertiCoach Lifestyle App for Subfertile Couples: Single-Center Evaluation of Augmented Standard Care %A Smeenk,Jesper %A Smit,Ellen %A Jacobs,Marc %A van Rooij,Ilse %+ Elisabeth TweeSteden Ziekenhuis, Doctor Deelenlaan 5, Tilburg, 5042 AD, The Netherlands, 31 (013) 221 00 00, j.smeenk@etz.nl %K fertility %K mHealth %K pregnancy %K lifestyle %K app %K smartphone %D 2025 %7 10.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Many couples undergoing fertility treatment face multiple lifestyle risk factors that lower their chances of achieving pregnancy. The MyFertiCoach (MFC) app was designed as an integrated lifestyle program featuring modules on healthy weight management, nutrition, exercise, quitting smoking, reducing alcohol and drug use, and managing stress. We hypothesized that supplementing standard care with the MFC app would improve lifestyle outcomes. Objective: This study aims to assess the impact of the MFC app on changing multiple lifestyle habits in women seeking fertility treatment. The primary outcome is the change in the total risk score (TRS) at 3- and six-month follow-ups. The TRS is calculated for each individual as the sum of all risk scores per behavior (eg, vegetable/fruit/folic acid intake, smoking, and alcohol use) at 3 and 6 months. A higher TRS indicates unhealthier nutrition and lifestyle habits and a lower likelihood of achieving pregnancy. The secondary endpoints include changes in BMI, activity score, preconception dietary risk score, distress score (eg, perceived burden), smoking habits, alcohol intake, and program adherence. Methods: This retrospective, observational, single-center evaluation included patients between January 1, 2022, and December 31, 2023. Subfertile female patients aged 18-43 years and their partners, who were referred to a gynecologist, were invited to participate in online lifestyle coaching via the MFC app. The gynecologist selected relevant lifestyle modules based on the results of integrated screening questionnaires. We used (hierarchical) linear mixed models (LMMs) to estimate changes in outcomes. For missing data patterns deemed missing not at random, joint modeling was applied. Statistical significance was set at P≤.05, with methods in place to maintain the same false-positive rate. Results: A total of 1805 patients were invited to participate in the evaluation, with an average of 737 (40.83%) completing the screening questionnaire at baseline. For the TRS, 798 (44.21%) patients were included at baseline, of whom 517 (64.8%) involved their partner. On average, 282 of 744 (37.9%) patients submitted at least one follow-up questionnaire. Patients rated the app above average (n=137, median score of 7 on a 1-10 scale) on days 7 and 14. The TRS decreased by an average of 1.5 points (P<.001) at T3 and T6 compared with baseline, a clinically meaningful improvement. All secondary outcomes showed statistically significant positive changes for patients who used a relevant lifestyle module (P<.001). Most improvements were achieved by 3 months and remained significant at 6 months (P<.001), except for alcohol intake (P<.53). These findings were consistent across both LMMs and joint models. Conclusions: Our evaluation of a mobile health app integrated into standard care demonstrates immediate and clinically meaningful improvements in key lifestyle parameters among women seeking to become pregnant. Additional scientific research is needed to identify the causal pathways leading to sustained effectiveness. To maintain and enhance these outcomes, further tailoring of patient-specific programs is essential. %M 40063944 %R 10.2196/64239 %U https://formative.jmir.org/2025/1/e64239 %U https://doi.org/10.2196/64239 %U http://www.ncbi.nlm.nih.gov/pubmed/40063944 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e65563 %T Risk Stratification in Immunoglobulin A Nephropathy Using Network Biomarkers: Development and Validation Study %A Tan,Jiaxing %A Yang,Rongxin %A Xiao,Liyin %A Dong,Lingqiu %A Zhong,Zhengxia %A Zhou,Ling %A Qin,Wei %+ Division of Nephrology, Department of Medicine, West China Hospital of Sichuan University, No 37 Guoxue Alley, Wuhou District, Chengdu, 610041, China, 86 18980602119, qinweihx@scu.edu.cn %K IgA nephropathy %K unsupervised learning %K network biomarker %K metabolomics %K gut microbiota %K biomarkers %K risk stratification %K IgA %K immunoglobulin A %K renal biopsy %K renal %K prospective cohort %K Berger disease %K synpharyngitic glomerulonephritis %K kidney %K immune system %K glomerulonephritis %K kidney inflammation %K chronic kidney disease %K renal disease %K nephropathy %K nephritis %D 2025 %7 10.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Traditional risk models for immunoglobulin A nephropathy (IgAN), which primarily rely on renal indicators, lack comprehensive assessment and therapeutic guidance, necessitating more refined and integrative approaches. Objective: This study integrated network biomarkers with unsupervised learning clustering (k-means clustering based on network biomarkers [KMN]) to refine risk stratification in IgAN and explore its clinical value. Methods: Involving a multicenter prospective cohort, we analyzed 1460 patients and validated the approach externally with 200 additional patients. Deeper metabolic and microbiomic insights were gained from 2 distinct cohorts: 63 patients underwent ultraperformance liquid chromatography–mass spectrometry, while another 45 underwent fecal 16S RNA sequencing. Our approach used hierarchical clustering and k-means methods, using 3 sets of indicators: demographic and renal indicators, renal and extrarenal indicators, and network biomarkers derived from all indicators. Results: Among 6 clustering methods tested, the KMN scheme was the most effective, accurately reflecting patient severity and prognosis with a prognostic accuracy area under the curve (AUC) of 0.77, achieved solely through cluster grouping without additional indicators. The KMN stratification significantly outperformed the existing International IgA Nephropathy Prediction Tool (AUC of 0.72) and renal function-renal histology grading schemes (AUC of 0.69). Clinically, this stratification facilitated personalized treatment, recommending angiotensin-converting enzyme inhibitors or angiotensin receptor blockers for lower-risk groups and considering immunosuppressive therapy for higher-risk groups. Preliminary findings also indicated a correlation between IgAN progression and alterations in serum metabolites and gut microbiota, although further research is needed to establish causality. Conclusions: The effectiveness and applicability of the KMN scheme indicate its substantial potential for clinical application in IgAN management. %M 40063072 %R 10.2196/65563 %U https://www.jmir.org/2025/1/e65563 %U https://doi.org/10.2196/65563 %U http://www.ncbi.nlm.nih.gov/pubmed/40063072 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 12 %N %P e71439 %T Development of a Patient-Centered Symptom-Reporting Application in Pharmacy Settings Using a Hierarchical Patient-Friendly Symptom List: Developmental and Usability Study %A Watanabe,Seiya %A Kizaki,Hayato %A Hori,Satoko %+ Division of Drug Informatics, Keio University Faculty of Pharmacy, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512, Japan, 81 354002650, satokoh@keio.jp %K patient symptom monitoring %K hierarchical symptom list %K community pharmacy %K interview survey %K mobile application %D 2025 %7 6.3.2025 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Effective symptom identification, a key responsibility for community pharmacists, requires patients to describe their symptoms accurately and comprehensively. However, current practices in pharmacies may be insufficient in capturing patient-reported symptoms comprehensively, potentially affecting the quality of pharmaceutical care and patient safety. Objective: This study aimed to construct a new, hierarchical symptom list derived from the Patient-Friendly Term List of the Medical Dictionary for Regulatory Activities (MedDRA) and to develop and evaluate a mobile app incorporating this list for facilitating symptom reporting by patients in pharmacy settings. The study also aimed to assess the usability and acceptance of this app among potential users. Methods: Subjective symptom-related terms were extracted from the Patient-Friendly Term List version 23.0 of the MedDRA. These terms were systematically consolidated and organized into a hierarchical, user-friendly symptom list. A mobile app incorporating this list was developed for pharmacy settings, featuring a symptom selection interface and a free-text input field for additional symptoms. The app included an instructional video explaining the importance of symptom reporting and guidance on navigation. Usability tests and semistructured interviews were conducted with participants aged >20 years. Interview transcripts were analyzed using the Unified Theory of Acceptance and Use of Technology (UTAUT) model to evaluate factors influencing the acceptance of technology. Results: From the initial 1440 terms in the Patient-Friendly Term List, 795 relevant terms were selected and organized into 40 site-specific subcategories, which were then grouped into broader site categories (mental, head, trunk, upper limb, lower limb, physical condition, and others). These terms were further consolidated into 211 patient-friendly symptom terms, forming a hierarchical symptom list. The app’s interface design limited options to 10 items per screen to assist with decision-making. A total of 5 adults participated in the usability test. Participants found the interface intuitive and easy to use, requiring minimal effort, and provided positive feedback regarding the potential utility of the app in pharmacy settings. The UTAUT analysis identified several facilitating factors, including ease of use and the potential for enhanced pharmacist-patient communication. However, concerns were raised about usability for older adults and the need for simplified technical terminology. Conclusions: The user-friendly app with a hierarchically structured symptom list and complementary free-text entry has potential benefits for improving the accuracy and efficiency of symptom reporting in pharmacy settings. The positive user acceptance and identified areas for improvement provide a foundation for further development and implementation of this technology to enhance communication between patients and pharmacists. Future improvements should focus on addressing usability for older adults and simplifying technical terminology. %M 40053749 %R 10.2196/71439 %U https://humanfactors.jmir.org/2025/1/e71439 %U https://doi.org/10.2196/71439 %U http://www.ncbi.nlm.nih.gov/pubmed/40053749 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e66744 %T Patients’ Experience With Evaluation by Both a Musculoskeletal Physician and Physical Therapist in the Same Digital Visit: Survey Study %A O'Connor,Mary I %A Chudy,Carolyn %A Peters,Kaitlyn C %A Ribaudo,Megan %A McCulloch,Carrie %A Aguilar,Jared %A Taylor,Trista %A Grant,Ryan A %K telemedicine %K musculoskeletal care %K patient satisfaction %K multidisciplinary care %K digital visit %D 2025 %7 3.3.2025 %9 %J JMIR Form Res %G English %X Background: Patients undergoing evaluation for musculoskeletal concerns are often seen by a physician and physical therapist in the in-person setting in a sequential manner. This process typically delays the onset of nonoperative care, inclusive of physical therapy, and creates the risk of inadequate clinical collaboration between physician and physical therapist. To address these issues, we designed a novel initial patient evaluation to a group visit in which both a specialty-trained musculoskeletal physician and physical therapist simultaneously evaluate a patient together in the digital encounter. Objective: The aim of the study is to gain insights from patients on their experience with this innovative digital simultaneous musculoskeletal medical doctor and physical therapist (MD+PT) visit format for the initial evaluation of musculoskeletal concerns. Methods: An electronic 7-question survey was sent to 750 patients who completed an MD+PT visit asking them to comment on prior musculoskeletal evaluations and their experience with the MD+PT format. Results: In total, 195 (26%) patients responded to the survey with the frequent body regions of diagnosis being lumbar spine (n=65), knee (n=32), shoulder (n=21), cervical spine (n=20), hip (n=14), and hand (n=11). Most patients had prior musculoskeletal experience with a physician or nurse practitioner (171/195, 87.7%) or physical therapist (148/195, 75.9%) with nearly all such encounters in the in-person setting (161/171,94.2% for physician or nurse practitioner and 144/148, 97.3% for physical therapy). Only 3.1% (6/193) of patients reported seeing both a physician and physical therapist during the same in-person visit. Patients rated the simultaneous MD+PT visit very favorably: this type of digital evaluation saved them time (179/192, 93.2%) and permitted them to promptly start their treatment plan (174/192, 90.6%). Overall, 87.5% (168/192) rated the MD+PT visit as enjoyable, and 92.2% (177/192) responded that it increased their confidence with understanding their medical condition and how to start treating it. Conclusions: Our early experience with the evaluation of patients with musculoskeletal conditions by both a specialty-trained musculoskeletal physician and physical therapist simultaneously in the same digital visit resulted in patients reporting a very positive experience with high satisfaction, engagement, and confidence in understanding their diagnosis and how to start treating it. %R 10.2196/66744 %U https://formative.jmir.org/2025/1/e66744 %U https://doi.org/10.2196/66744 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 8 %N %P e60038 %T Usability and Acceptability of a Pregnancy App for Substance Use Screening and Education: A Mixed Methods Exploratory Pilot Study %A Fitzgerald,Haley %A Frank,Madison %A Kasula,Katelyn %A Krans,Elizabeth E %A Krishnamurti,Tamar %K substance use disorder %K substance use screening %K mHealth %K mobile health apps %K pregnancy %K technology %D 2025 %7 13.2.2025 %9 %J JMIR Pediatr Parent %G English %X Background: Increasing opioid and other substance use has led to a crisis of epidemic proportions, with substance use now recognized as a leading cause of maternal morbidity and mortality in the United States. Interventions will only be effective if those who would benefit are identified early and connected to care. Apps are a ubiquitous source of pregnancy information, but their utility as a platform for evaluating substance use during pregnancy is unknown. Objective: This study aims to explore the usability and acceptability of a pregnancy app for opioid and other substance use screening and education. Methods: This mixed methods, exploratory pilot study examined adult pregnant people with a history of substance use who were recruited from outpatient and inpatient settings at a tertiary care obstetric hospital. After completing a baseline survey collecting demographics, substance use, and technology use, participants accessed an existing pregnancy support app for 4 weeks. Qualitative methods were used to measure the acceptability of embedding substance use screening, education, and information within the tool. App use frequency and access to substance use educational content and treatment referral information were evaluated. Results: The 28 female participants had a mean (SD) age of 31 (0.46) years; most were White (21/28, 75%) and Medicaid insured (26/28, 93%), with an annual household income of