Journal of Medical Internet Research最新文献

{"title":"Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study.","authors":"Shuai Ming, Xi Yao, Xiaohong Guo, Qingge Guo, Kunpeng Xie, Dandan Chen, Bo Lei","doi":"10.2196/60226","DOIUrl":"https://doi.org/10.2196/60226","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) chatbots such as ChatGPT are expected to impact vision health care significantly. Their potential to optimize the consultation process and diagnostic capabilities across range of ophthalmic subspecialties have yet to be fully explored.</p><p><strong>Objective: </strong>This study aims to investigate the performance of AI chatbots in recommending ophthalmic outpatient registration and diagnosing eye diseases within clinical case profiles.</p><p><strong>Methods: </strong>This cross-sectional study used clinical cases from Chinese Standardized Resident Training-Ophthalmology (2nd Edition). For each case, 2 profiles were created: patient with history (Hx) and patient with history and examination (Hx+Ex). These profiles served as independent queries for GPT-3.5 and GPT-4.0 (accessed from March 5 to 18, 2024). Similarly, 3 ophthalmic residents were posed the same profiles in a questionnaire format. The accuracy of recommending ophthalmic subspecialty registration was primarily evaluated using Hx profiles. The accuracy of the top-ranked diagnosis and the accuracy of the diagnosis within the top 3 suggestions (do-not-miss diagnosis) were assessed using Hx+Ex profiles. The gold standard for judgment was the published, official diagnosis. Characteristics of incorrect diagnoses by ChatGPT were also analyzed.</p><p><strong>Results: </strong>A total of 208 clinical profiles from 12 ophthalmic subspecialties were analyzed (104 Hx and 104 Hx+Ex profiles). For Hx profiles, GPT-3.5, GPT-4.0, and residents showed comparable accuracy in registration suggestions (66/104, 63.5%; 81/104, 77.9%; and 72/104, 69.2%, respectively; P=.07), with ocular trauma, retinal diseases, and strabismus and amblyopia achieving the top 3 accuracies. For Hx+Ex profiles, both GPT-4.0 and residents demonstrated higher diagnostic accuracy than GPT-3.5 (62/104, 59.6% and 63/104, 60.6% vs 41/104, 39.4%; P=.003 and P=.001, respectively). Accuracy for do-not-miss diagnoses also improved (79/104, 76% and 68/104, 65.4% vs 51/104, 49%; P<.001 and P=.02, respectively). The highest diagnostic accuracies were observed in glaucoma; lens diseases; and eyelid, lacrimal, and orbital diseases. GPT-4.0 recorded fewer incorrect top-3 diagnoses (25/42, 60% vs 53/63, 84%; P=.005) and more partially correct diagnoses (21/42, 50% vs 7/63 11%; P<.001) than GPT-3.5, while GPT-3.5 had more completely incorrect (27/63, 43% vs 7/42, 17%; P=.005) and less precise diagnoses (22/63, 35% vs 5/42, 12%; P=.009).</p><p><strong>Conclusions: </strong>GPT-3.5 and GPT-4.0 showed intermediate performance in recommending ophthalmic subspecialties for registration. While GPT-3.5 underperformed, GPT-4.0 approached and numerically surpassed residents in differential diagnosis. AI chatbots show promise in facilitating ophthalmic patient registration. However, their integration into diagnostic decision-making requires more validation.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e60226"},"PeriodicalIF":5.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economics and Equity of Large Language Models: Health Care Perspective.","authors":"Radha Nagarajan, Midori Kondo, Franz Salas, Emre Sezgin, Yuan Yao, Vanessa Klotzman, Sandip A Godambe, Naqi Khan, Alfonso Limon, Graham Stephenson, Sharief Taraman, Nephi Walton, Louis Ehwerhemuepha, Jay Pandit, Deepti Pandita, Michael Weiss, Charles Golden, Adam Gold, John Henderson, Angela Shippy, Leo Anthony Celi, William R Hogan, Eric K Oermann, Terence Sanger, Steven Martel","doi":"10.2196/64226","DOIUrl":"https://doi.org/10.2196/64226","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Large language models (LLMs) continue to exhibit noteworthy capabilities across a spectrum of areas, including emerging proficiencies across the health care continuum. Successful LLM implementation and adoption depend on digital readiness, modern infrastructure, a trained workforce, privacy, and an ethical regulatory landscape. These factors can vary significantly across health care ecosystems, dictating the choice of a particular LLM implementation pathway. This perspective discusses 3 LLM implementation pathways-training from scratch pathway (TSP), fine-tuned pathway (FTP), and out-of-the-box pathway (OBP)-as potential onboarding points for health systems while facilitating equitable adoption. The choice of a particular pathway is governed by needs as well as affordability. Therefore, the risks, benefits, and economics of these pathways across 4 major cloud service providers (Amazon, Microsoft, Google, and Oracle) are presented. While cost comparisons, such as on-demand and spot pricing across the cloud service providers for the 3 pathways, are presented for completeness, the usefulness of managed services and cloud enterprise tools is elucidated. Managed services can complement the traditional workforce and expertise, while enterprise tools, such as federated learning, can overcome sample size challenges when implementing LLMs using health care data. Of the 3 pathways, TSP is expected to be the most resource-intensive regarding infrastructure and workforce while providing maximum customization, enhanced transparency, and performance. Because TSP trains the LLM using enterprise health care data, it is expected to harness the digital signatures of the population served by the health care system with the potential to impact outcomes. The use of pretrained models in FTP is a limitation. It may impact its performance because the training data used in the pretrained model may have hidden bias and may not necessarily be health care-related. However, FTP provides a balance between customization, cost, and performance. While OBP can be rapidly deployed, it provides minimal customization and transparency without guaranteeing long-term availability. OBP may also present challenges in interfacing seamlessly with downstream applications in health care settings with variations in pricing and use over time. Lack of customization in OBP can significantly limit its ability to impact outcomes. Finally, potential applications of LLMs in health care, including conversational artificial intelligence, chatbots, summarization, and machine translation, are highlighted. While the 3 implementation pathways discussed in this perspective have the potential to facilitate equitable adoption and democratization of LLMs, transitions between them may be necessary as the needs of health systems evolve. Understanding the economics and trade-offs of these onboarding pathways can guide their strategic adoption and demonstrate value while impacting health care outcomes favor","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e64226"},"PeriodicalIF":5.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review.","authors":"David Drummond, Apolline Gonsard","doi":"10.2196/58504","DOIUrl":"https://doi.org/10.2196/58504","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The concept of digital twins, widely adopted in industry, is entering health care. However, there is a lack of consensus on what constitutes the digital twin of a patient.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objective of this scoping review was to analyze definitions and characteristics of patient digital twins being developed for clinical use, as reported in the scientific literature.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We searched PubMed, Scopus, Embase, IEEE, and Google Scholar for studies claiming digital twin development or evaluation until August 2023. Data on definitions, characteristics, and development phase were extracted. Unsupervised classification of claimed digital twins was performed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We identified 86 papers representing 80 unique claimed digital twins, with 98% (78/80) in preclinical phases. Among the 55 papers defining \"digital twin,\" 76% (42/55) described a digital replica, 42% (23/55) mentioned real-time updates, 24% (13/55) emphasized patient specificity, and 15% (8/55) included 2-way communication. Among claimed digital twins, 60% (48/80) represented specific organs (primarily heart: 15/48, 31%; bones or joints: 10/48, 21%; lung: 6/48, 12%; and arteries: 5/48, 10%); 14% (11/80) embodied biological systems such as the immune system; and 26% (21/80) corresponded to other products (prediction models, etc). The patient data used to develop and run the claimed digital twins encompassed medical imaging examinations (35/80, 44% of publications), clinical notes (15/80, 19% of publications), laboratory test results (13/80, 16% of publications), wearable device data (12/80, 15% of publications), and other modalities (32/80, 40% of publications). Regarding data flow between patients and their virtual counterparts, 16% (13/80) claimed that digital twins involved no flow from patient to digital twin, 73% (58/80) used 1-way flow from patient to digital twin, and 11% (9/80) enabled 2-way data flow between patient and digital twin. Based on these characteristics, unsupervised classification revealed 3 clusters: simulation patient digital twins in 54% (43/80) of publications, monitoring patient digital twins in 28% (22/80) of publications, and research-oriented models unlinked to specific patients in 19% (15/80) of publications. Simulation patient digital twins used computational modeling for personalized predictions and therapy evaluations, mostly for one-time assessments, and monitoring digital twins harnessed aggregated patient data for continuous risk or outcome forecasting and care optimization.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;We propose defining a patient digital twin as \"a viewable digital replica of a patient, organ, or biological system that contains multidimensional, patient-specific information and informs decisions\" and to distinguish simulation and monitoring digital twins. These proposed definitions and subtypes offer a framework to guide research into realizing t","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e58504"},"PeriodicalIF":5.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differences in Use of a Patient Portal Across Sociodemographic Groups: Observational Study of the NHS App in England.","authors":"Sukriti Kc, Chrysanthi Papoutsi, Claire Reidy, Bernard Gudgin, John Powell, Azeem Majeed, Felix Greaves, Anthony A Laverty","doi":"10.2196/56320","DOIUrl":"https://doi.org/10.2196/56320","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The adoption of patient portals, such as the National Health Service (NHS) App in England, may improve patient engagement in health care. However, concerns remain regarding differences across sociodemographic groups in the uptake and use of various patient portal features, which have not been fully explored. Understanding the use of various functions across diverse populations is essential to ensure any benefits are equally distributed across the population.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to explore differences in the use of NHS App features across age, sex, deprivation, ethnicity, long-term health care needs, and general practice (GP) size categories.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We used weekly NHS App use data from the NHS App dashboard for 6386 GPs in England from March 2020 to June 2022. Negative binomial regression models explored variations in weekly rates of NHS App features used (registrations, log-ins, prescriptions ordered, medical record views, and appointments booked). Outcomes were measured as weekly rates per 1000 GP-registered patients, and we conducted separate models for each outcome. Regression models included all covariates mentioned above and produced incident rate ratios, which we present here as relative percentages for ease of interpretation. GP-level covariate data on sociodemographic variables were used as categorical variables in 5 groups for deprivation (Q1=least deprived practices and Q5=most deprived practices) and 4 groups for all other variables (Q1=least deprived practices and Q4=most deprived practices).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We found variations in the use of different features overall and across sociodemographic categories. Fully adjusted regression models found lower use of features overall in more deprived practices (eg, Q5 vs Q1: registrations=-34%, log-ins=-34.9%, appointments booked=-39.7%, medical record views=-32.3%, and prescriptions ordered=-9.9%; P&lt;.001). Practices with greater proportions of male patients also had lower levels of NHS App use (eg, Q4 vs Q1: registration=-7.1%, log-in=-10.4%, and appointments booked=-36.4%; P&lt;.001). Larger practices had an overall higher use of some NHS App features (eg, Q4 vs Q1: registration=3.2%, log-ins=11.7%, appointments booked=73.4%, medical record views=23.9%, and prescriptions ordered=20.7%; P&lt;.001), as well as those with greater proportions of White patients (eg, Q4 vs Q1: registration=1.9%, log-ins=9.1%, appointments booked=14.1%, medical record views=28.7%, and prescriptions ordered=130.4%; P&lt;.001). Use patterns varied for practices with greater proportions of patients with long-term health care needs (eg, Q4 vs Q1: registrations=-3.6%, appointments booked=-20%, and medical record views=6%; P≤.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study highlights that the use of the NHS App features varied across sociodemographic groups. In particular, it is used less by people living in more deprived a","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e56320"},"PeriodicalIF":5.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Barriers and Facilitators Associated With Remote Concussion Physical Assessments From the Perspectives of Clinicians and People Living With Workplace Concussions: Focus Group Study.","authors":"Keely Barnes, Heidi Sveistrup, Motahareh Karimijashni, Mark Bayley, Mary Egan, Martin Bilodeau, Michel Rathbone, Monica Taljaard, Shawn Marshall","doi":"10.2196/56158","DOIUrl":"https://doi.org/10.2196/56158","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Evaluating the clinical status of concussions using virtual platforms has become increasingly common. While virtual approaches to care are useful, there is limited information regarding the barriers and facilitators associated with a virtual concussion assessment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to identify the barriers and facilitators associated with engaging in virtual concussion assessments from the perspective of people living with workplace concussions; identify the barriers and facilitators to completing virtual concussion assessments from the perspectives of clinicians; and identify the clinical measures related to 4 clinical domains that would be most appropriate in virtual practice: general neurological examination and vestibular, oculomotor, and cervical spine assessment. We also evaluated effort.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Separate online focus groups were conducted with expert concussion clinicians and people living with workplace concussions. A moderator led the focus groups using a semistructured interview guide that targeted a discussion of participants' experiences with virtual assessments. The discussions were recorded, transcribed, and analyzed by 2 reviewers using content analysis. Barriers and facilitators associated with completing the physical concussion examination were categorized based on the domain of the concussion examination and more general barriers and facilitators. Clinician-selected measures believed to work best in a virtual practice were described using frequency counts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 4 focus groups with 15 people living with workplace concussions and 3 focus groups with 14 clinicians were completed using Microsoft Teams. Barriers were identified, such as triggering of symptoms associated with completing an assessment over video (mentioned 13/162 (8%) and 9/201 (4%) of the time for patient and clinician participants, respectively); challenges with location and setup (mentioned 16/162 (10%) of the time for patient participants); communication (mentioned 34/162 (21%) and 9/201 (4%) of the time for patient and clinician participants, respectively); and safety concerns (mentioned 11/162 (7%) of the time for patient and 15/201 (7%) for clinician participants). Facilitators were identified, such as having access to support (mentioned 42/154 (27%) and 21/151 (14%) of the time for patient and clinician participants, respectively); implementing symptom management strategies throughout the assessment (mentioned 11/154 (7%) of the time for patient participants); and having access to resources (mentioned 25/151 (17%) of the time for clinician participants). From the perspective of the clinician participants included in this study, the clinical measures recommended most for a virtual practice were finger to nose testing; balance testing; the Vestibular/Ocular Motor Screening tool; saccades; and cervical spine range of motion within their res","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e56158"},"PeriodicalIF":5.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142620599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.","authors":"Minseok Hong, Ri-Ra Kang, Jeong Hun Yang, Sang Jin Rhee, Hyunju Lee, Yong-Gyom Kim, KangYoon Lee, HongGi Kim, Yu Sang Lee, Tak Youn, Se Hyun Kim, Yong Min Ahn","doi":"10.2196/65994","DOIUrl":"https://doi.org/10.2196/65994","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Assessing the complex and multifaceted symptoms of patients with acute psychiatric disorders proves to be significantly challenging for clinicians. Moreover, the staff in acute psychiatric wards face high work intensity and risk of burnout, yet research on the introduction of digital technologies in this field remains limited. The combination of continuous and objective wearable sensor data acquired from patients with deep learning techniques holds the potential to overcome the limitations of traditional psychiatric assessments and support clinical decision-making.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop and validate wearable-based deep learning models to comprehensively predict patient symptoms across various acute psychiatric wards in South Korea.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Participants diagnosed with schizophrenia and mood disorders were recruited from 4 wards across 3 hospitals and prospectively observed using wrist-worn wearable devices during their admission period. Trained raters conducted periodic clinical assessments using the Brief Psychiatric Rating Scale, Hamilton Anxiety Rating Scale, Montgomery-Asberg Depression Rating Scale, and Young Mania Rating Scale. Wearable devices collected patients' heart rate, accelerometer, and location data. Deep learning models were developed to predict psychiatric symptoms using 2 distinct approaches: single symptoms individually (Single) and multiple symptoms simultaneously via multitask learning (Multi). These models further addressed 2 problems: within-subject relative changes (Deterioration) and between-subject absolute severity (Score). Four configurations were consequently developed for each scale: Single-Deterioration, Single-Score, Multi-Deterioration, and Multi-Score. Data of participants recruited before May 1, 2024, underwent cross-validation, and the resulting fine-tuned models were then externally validated using data from the remaining participants.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 244 enrolled participants, 191 (78.3%; 3954 person-days) were included in the final analysis after applying the exclusion criteria. The demographic and clinical characteristics of participants, as well as the distribution of sensor data, showed considerable variations across wards and hospitals. Data of 139 participants were used for cross-validation, while data of 52 participants were used for external validation. The Single-Deterioration and Multi-Deterioration models achieved similar overall accuracy values of 0.75 in cross-validation and 0.73 in external validation. The Single-Score and Multi-Score models attained overall R² values of 0.78 and 0.83 in cross-validation and 0.66 and 0.74 in external validation, respectively, with the Multi-Score model demonstrating superior performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Deep learning models based on wearable sensor data effectively classified symptom deterioration and predicted symptom severit","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e65994"},"PeriodicalIF":5.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clarifying the Concepts of Personalization and Tailoring of eHealth Technologies: Multimethod Qualitative Study.","authors":"Iris Ten Klooster, Hanneke Kip, Sina L Beyer, Lisette J E W C van Gemert-Pijnen, Saskia M Kelders","doi":"10.2196/50497","DOIUrl":"https://doi.org/10.2196/50497","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Although personalization and tailoring have been identified as alternatives to a \"one-size-fits-all\" approach for eHealth technologies, there is no common understanding of these two concepts and how they should be applied.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to describe (1) how tailoring and personalization are defined in the literature and by eHealth experts, and what the differences and similarities are; (2) what type of variables can be used to segment eHealth users into more homogeneous groups or at the individual level; (3) what elements of eHealth technologies are adapted to these segments; and (4) how the segments are matched with eHealth adaptations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We used a multimethod qualitative study design. To gain insights into the definitions of personalization and tailoring, definitions were collected from the literature and through interviews with eHealth experts. In addition, the interviews included questions about how users can be segmented and how eHealth can be adapted accordingly, and responses to 3 vignettes of examples of eHealth technologies, varying in personalization and tailoring strategies to elicit responses about views from stakeholders on how the two components were applied and matched in different contexts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 28 unique definitions of tailoring and 16 unique definitions of personalization were collected from the literature and interviews. The definitions of tailoring and personalization varied in their components, namely adaptation, individuals, user groups, preferences, symptoms, characteristics, context, behavior, content, identification, feedback, channel, design, computerization, and outcomes. During the interviews, participants mentioned 9 types of variables that can be used to segment eHealth users, namely demographics, preferences, health variables, psychological variables, behavioral variables, individual determinants, environmental information, intervention interaction, and technology variables. In total, 5 elements were mentioned that can be adapted to those segments, namely channeling, content, graphical, functionalities, and behavior change strategy. Participants mentioned substantiation methods and variable levels as two components for matching the segmentations with adaptations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Tailoring and personalization are multidimensional concepts, and variability and technology affordances seem to determine whether and how personalization and tailoring should be applied to eHealth technologies. On the basis of our findings, tailoring and personalization can be differentiated by the way that segmentations and adaptations are matched. Tailoring matches segmentations and adaptations based on general group characteristics using if-then algorithms, whereas personalization involves the direct insertion of user information (such as name) or adaptations based on individual-level i","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e50497"},"PeriodicalIF":5.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142620625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Electronic Medical Record-Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation.","authors":"Rex Parsons, Robin Blythe, Susanna Cramb, Ahmad Abdel-Hafez, Steven McPhail","doi":"10.2196/59634","DOIUrl":"https://doi.org/10.2196/59634","url":null,"abstract":"<p><strong>Background: </strong>Effective fall prevention interventions in hospitals require appropriate allocation of resources early in admission. To address this, fall risk prediction tools and models have been developed with the aim to provide fall prevention strategies to patients at high risk. However, fall risk assessment tools have typically been inaccurate for prediction, ineffective in prevention, and time-consuming to complete. Accurate, dynamic, individualized estimates of fall risk for admitted patients using routinely recorded data may assist in prioritizing fall prevention efforts.</p><p><strong>Objective: </strong>The objective of this study was to develop and validate an accurate and dynamic prognostic model for inpatient falls among a cohort of patients using routinely recorded electronic medical record data.</p><p><strong>Methods: </strong>We used routinely recorded data from 5 Australian hospitals to develop and internally-externally validate a prediction model for inpatient falls using a Cox proportional hazards model with time-varying covariates. The study cohort included patients admitted during 2018-2021 to any ward, with no age restriction. Predictors used in the model included admission-related administrative data, length of stay, and number of previous falls during the admission (updated every 12 hours up to 14 days after admission). Model calibration was assessed using Poisson regression and discrimination using the area under the time-dependent receiver operating characteristic curve.</p><p><strong>Results: </strong>There were 1,107,556 inpatient admissions, 6004 falls, and 5341 unique fallers. The area under the time-dependent receiver operating characteristic curve was 0.899 (95% CI 0.88-0.91) at 24 hours after admission and declined throughout admission (eg, 0.765, 95% CI 0.75-0.78 on the seventh day after admission). Site-dependent overestimation and underestimation of risk was observed on the calibration plots.</p><p><strong>Conclusions: </strong>Using a large dataset from multiple hospitals and robust methods to model development and validation, we developed a prognostic model for inpatient falls. It had high discrimination, suggesting the model has the potential for operationalization in clinical decision support for prioritizing inpatients for fall prevention. Performance was site dependent, and model recalibration may lead to improved performance.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e59634"},"PeriodicalIF":5.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142620329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effects of Virtual Reality-Based Reminiscence Therapies for Older Adults With Cognitive Impairment: Systematic Review.","authors":"Qian Mao, Zhen Zhao, Lisha Yu, Yang Zhao, Hailiang Wang","doi":"10.2196/53348","DOIUrl":"10.2196/53348","url":null,"abstract":"<p><strong>Background: </strong>Reminiscence therapy (RT) is a commonly used nonpharmaceutical treatment for cognitive impairment. Artifacts or conversations are used in RT to recall individuals' memories and past experiences. Virtual reality (VR) has increasingly been used as an assistive technology during RT. However, the effects of VR-based RT (VR-RT) methods remain unclear, and insights into the related benefits and challenges are urgently needed.</p><p><strong>Objective: </strong>The study aims to systematically review the effects of VR-RTs for older adults with cognitive impairment.</p><p><strong>Methods: </strong>Seven databases (MEDLINE, Academic Search Premier, CINAHL, Web of Science, PubMed, the Cochrane Central Register of Controlled Trials, and ScienceDirect) were searched to identify relevant articles published from inception to August 10, 2023. Peer-reviewed publications that assessed the effect of VR-RTs (ie, using virtual clues to evoke participants' memories or past experiences) on cognitive-related outcomes were included. Two independent researchers conducted the literature search, review, and data extraction processes. A narrative synthesis approach was used to analyze the extracted data.</p><p><strong>Results: </strong>Of the 537 identified articles, 22 were ultimately included in the data analysis. The results revealed that VR-RTs could maintain cognitive status (4/4, 100%) and reduce anxiety (2/2, 100%) in older adults with cognitive impairment. Nevertheless, one study found a cognitive improvement after VR-RTs, whereas cognitive degradation was observed at a 3- to 6-month follow-up measure. Around 88% (7/8) of the included studies indicated that VR-RTs improved memory; however, the evidence regarding the beneficial effects of VR-RTs was limited in improving quality of life (1/4, 25%) and reducing apathy (0/2, 0%) and depression (1/3, 33%). The results indicated that VR-RTs are safe, engaging, acceptable, and satisfying for older adults with cognitive impairment. In VR scenarios, personalized stimulus materials related to the users' youth experiences were more effective for treating cognitive impairment than other stimulus materials.</p><p><strong>Conclusions: </strong>The results of this systematic review demonstrate the potential benefits of VR-RT for older adults with cognitive impairment, especially in improving emotion and memory and maintaining cognitive status. VR-RT is also safe and enjoyable for older adults. However, due to the trial heterogeneity of included studies, we can only provide qualitative results instead of performing meta-analysis to quantify the effect size of VR-RTs. Thus, more randomized controlled trials are required to examine the designs and effects of VR-RTs for groups of older adults with specific needs.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e53348"},"PeriodicalIF":5.8,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Internet Use Maintains Cognitive Function among middle- and old-aged Chinese: A Five-year Longitudinal Study.","authors":"Bowen Chen, Chun Yang, Shanshan Ren, Penggao Li, Jin Zhao","doi":"10.2196/57301","DOIUrl":"https://doi.org/10.2196/57301","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cognitive decline poses one of the greatest global challenges for health and social care, particularly in China, where the burden on the elderly population is most pronounced. Despite the rapid expansion of internet access, there is still limited understanding of the long-term cognitive impacts of internet usage among middle-aged and elderly individuals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to explore the association between internet usage and age-related cognitive decline among middle- and old-aged Chinese. To gain a more comprehensive understanding of the effects of internet usage, we also focused on assessing the impact of both the frequency of internet use and the types of internet devices on cognition. Moreover, we assessed the mediating role of internet usage on cognitive function for characteristics significantly linked to cognition in stratified analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed data based on 12,770 dementia-free participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study. We employed a fixed-effects model to assess the relationship between internet usage and cognitive decline, and further validated it using multiple linear regression, generalized estimating equations (GEE), propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and overlap weighting (OW). We further examined the varying effects of internet device type and frequency on cognitive function using fixed-effects models and Spearman's rank correlation. The Karlson-Holm-Breen (KHB) method was used to estimate the mediating role of internet usage in the urban-rural cognitive gap.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Participants using the internet (n=1,005) were younger, more likely to be male, more educated, married, retired, living in an urban area, and had higher cognitive assessment scores compared with non-users (n=11,765). After adjusting for demographic and health-related risk factors, there was a positive correlation between internet use and cognitive function (β=0.551, 95% CI 0.391 to 0.710). Over the follow-up period, persistent internet users had a markedly lower 5-year incidence of neurodegenerative diseases at 2.2% (15/671) compared with non-users at 5.3% (379/7,099; P&lt;.001). The negative impact of aging (&gt; 50) on cognitive function was consistently less pronounced among internet users compared to non-users. Furthermore, increased frequency of internet usage was associated with greater cognitive benefits for middle-aged and elderly individuals (rs = 0.378, P&lt;.001). Among digital devices used for internet access, cellphones (β=0.398, 95% CI 0.283 to 0.495) seem to have a higher level of cognitive protection compared to computers (β=0.147, 95% CI 0.091 to 0.204). The urban-rural disparity in cognitive function was partially attributed to the disparity in internet use (34.2% of the total effects, P&lt;0.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study revea","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142622442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}