JMIR Aging最新文献

{"title":"Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study.","authors":"Chang-Uk Jeong, Jacob S Leiby, Dokyoon Kim, Eun Kyung Choe","doi":"10.2196/64473","DOIUrl":"https://doi.org/10.2196/64473","url":null,"abstract":"<p><strong>Background: </strong>The global increase in life expectancy has not shown a similar rise in healthy life expectancy. Accurate assessment of biological aging is crucial for mitigating diseases and socioeconomic burdens associated with aging. Current biological age prediction models are limited by their reliance on conventional statistical methods and constrained clinical information.</p><p><strong>Objective: </strong>This study aimed to develop and validate an aging clock model using artificial intelligence, based on comprehensive health check-up data, to predict biological age and assess its clinical relevance.</p><p><strong>Methods: </strong>We used data from Koreans who underwent health checkups at the Seoul National University Hospital Gangnam Center as well as from the Korean Genome and Epidemiology Study. Our model incorporated 27 clinical factors and employed machine learning algorithms, including linear regression, least absolute shrinkage and selection operator, ridge regression, elastic net, random forest, support vector machine, gradient boosting, and K-nearest neighbors. Model performance was evaluated using adjusted R2 and the mean squared error (MSE) values. Shapley Additive exPlanation (SHAP) analysis was conducted to interpret the model's predictions.</p><p><strong>Results: </strong>The Gradient Boosting model achieved the best performance with a mean (SE) MSE of 4.219 (0.14) and a mean (SE) R2 of 0.967 (0.001). SHAP analysis identified significant predictors of biological age, including kidney function markers, gender, glycated hemoglobin level, liver function markers, and anthropometric measurements. After adjusting for the chronological age, the predicted biological age showed strong associations with multiple clinical factors, such as metabolic status, body compositions, fatty liver, smoking status, and pulmonary function.</p><p><strong>Conclusions: </strong>Our aging clock model demonstrates a high predictive accuracy and clinical relevance, offering a valuable tool for personalized health monitoring and intervention. The model's applicability in routine health checkups could enhance health management and promote regular health evaluations.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e64473"},"PeriodicalIF":5.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study.","authors":"Vimig Socrates, Donald S Wright, Thomas Huang, Soraya Fereydooni, Christine Dien, Ling Chi, Jesse Albano, Brian Patterson, Naga Sasidhar Kanaparthy, Catherine X Wright, Andrew Loza, David Chartash, Mark Iscoe, Richard Andrew Taylor","doi":"10.2196/69504","DOIUrl":"https://doi.org/10.2196/69504","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Polypharmacy, the concurrent use of multiple medications, is prevalent among older adults and associated with increased risks for adverse drug events including falls. Deprescribing, the systematic process of discontinuing potentially inappropriate medications, aims to mitigate these risks. However, the practical application of deprescribing criteria in emergency settings remains limited due to time constraints and criteria complexity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to evaluate the performance of a large language model (LLM)-based pipeline in identifying deprescribing opportunities for older emergency department (ED) patients with polypharmacy, using 3 different sets of criteria: Beers, Screening Tool of Older People's Prescriptions, and Geriatric Emergency Medication Safety Recommendations. The study further evaluates LLM confidence calibration and its ability to improve recommendation performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a retrospective cohort study of older adults presenting to an ED in a large academic medical center in the Northeast United States from January 2022 to March 2022. A random sample of 100 patients (712 total oral medications) was selected for detailed analysis. The LLM pipeline consisted of two steps: (1) filtering high-yield deprescribing criteria based on patients' medication lists, and (2) applying these criteria using both structured and unstructured patient data to recommend deprescribing. Model performance was assessed by comparing model recommendations to those of trained medical students, with discrepancies adjudicated by board-certified ED physicians. Selective prediction, a method that allows a model to abstain from low-confidence predictions to improve overall reliability, was applied to assess the model's confidence and decision-making thresholds.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The LLM was significantly more effective in identifying deprescribing criteria (positive predictive value: 0.83; negative predictive value: 0.93; McNemar test for paired proportions: χ&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;1&lt;/sub&gt;=5.985; P=.02) relative to medical students, but showed limitations in making specific deprescribing recommendations (positive predictive value=0.47; negative predictive value=0.93). Adjudication revealed that while the model excelled at identifying when there was a deprescribing criterion related to one of the patient's medications, it often struggled with determining whether that criterion applied to the specific case due to complex inclusion and exclusion criteria (54.5% of errors) and ambiguous clinical contexts (eg, missing information; 39.3% of errors). Selective prediction only marginally improved LLM performance due to poorly calibrated confidence estimates.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study highlights the potential of LLMs to support deprescribing decisions in the ED by effectively filtering relevant criteria. However, challenges remain in applyin","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e69504"},"PeriodicalIF":5.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12032504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-Related Barriers to Digital Technology Adoption in Alzheimer Disease: Systematic Review.","authors":"Andrea Panzavolta, Andrea Arighi, Emanuele Guido, Luigi Lavorgna, Francesco Di Lorenzo, Alessandra Dodich, Chiara Cerami","doi":"10.2196/64324","DOIUrl":"https://doi.org/10.2196/64324","url":null,"abstract":"<p><strong>Background: </strong>Digital technology in dementia is an area of great development with varying experiences across countries. However, novel digital solutions often lack a patient-oriented perspective, and several relevant barriers prevent their use in clinics.</p><p><strong>Objective: </strong>In this study, we reviewed the existing literature on knowledge, familiarity, and competence in using digital technology and on attitude and experiences with digital tools in Alzheimer disease. The main research question is whether digital competence and attitudes of patients and caregivers may affect the adoption of digital technology.</p><p><strong>Methods: </strong>Following the PRISMA guidelines, a literature search was conducted by two researchers in the group. Inter-rater reliability was calculated with Cohen κ statistics. The risk of bias assessment was also recorded.</p><p><strong>Results: </strong>Of 597 initial records, only 18 papers were considered eligible. Analyses of inter-rater reliability showed good agreement levels. Significant heterogeneity in study design, sample features, and measurement tools emerged across studies. Quality assessment showed a middle-high overall quality of evidence. The main factors affecting the adoption of digital technology in patients and caregivers are severity of cognitive deficits, timing of adoption, and the availability of training and support. Additional factors are age, type of digital device, and ease of use of the digital solution.</p><p><strong>Conclusions: </strong>Adoption of digital technology in dementia is hampered by many patient-related barriers. Improving digital competence in patient-caregiver dyads and implementing systematic, patient-oriented strategies for the development and use of digital tools are needed for a successful incorporation of digital technology in memory clinics.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e64324"},"PeriodicalIF":5.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144030826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-Based Feature Extraction and Classification for Parkinson Disease Screening Using Gait Analysis: Development and Validation Study.","authors":"Ming De Lim, Tee Connie, Michael Kah Ong Goh, Nor 'Izzati Saedon","doi":"10.2196/65629","DOIUrl":"10.2196/65629","url":null,"abstract":"<p><strong>Background: </strong>Parkinson disease (PD) is a progressive neurodegenerative disorder that affects motor coordination, leading to gait abnormalities. Early detection of PD is crucial for effective management and treatment. Traditional diagnostic methods often require invasive procedures or are performed when the disease has significantly progressed. Therefore, there is a need for noninvasive techniques that can identify early motor symptoms, particularly those related to gait.</p><p><strong>Objective: </strong>The study aimed to develop a noninvasive approach for the early detection of PD by analyzing model-based gait features. The primary focus is on identifying subtle gait abnormalities associated with PD using kinematic characteristics.</p><p><strong>Methods: </strong>Data were collected through controlled video recordings of participants performing the timed up and go (TUG) assessment, with particular emphasis on the turning phase. The kinematic features analyzed include shoulder distance, step length, stride length, knee and hip angles, leg and arm symmetry, and trunk angles. These features were processed using advanced filtering techniques and analyzed through machine learning methods to distinguish between normal and PD-affected gait patterns.</p><p><strong>Results: </strong>The analysis of kinematic features during the turning phase of the TUG assessment revealed that individuals with PD exhibited subtle gait abnormalities, such as freezing of gait, reduced step length, and asymmetrical movements. The model-based features proved effective in differentiating between normal and PD-affected gait, demonstrating the potential of this approach in early detection.</p><p><strong>Conclusions: </strong>This study presents a promising noninvasive method for the early detection of PD by analyzing specific gait features during the turning phase of the TUG assessment. The findings suggest that this approach could serve as a sensitive and accurate tool for diagnosing and monitoring PD, potentially leading to earlier intervention and improved patient outcomes.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e65629"},"PeriodicalIF":5.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12015338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Predictive Dashboard With Prescriptive Decision Support for Falls Prevention in Residential Aged Care: User-Centered Design Approach.","authors":"S Sandun Malpriya Silva, Nasir Wabe, Amy D Nguyen, Karla Seaman, Guogui Huang, Laura Dodds, Isabelle Meulenbroeks, Crisostomo Ibarra Mercado, Johanna I Westbrook","doi":"10.2196/63609","DOIUrl":"10.2196/63609","url":null,"abstract":"<p><strong>Background: </strong>Falls are a prevalent and serious health condition among older people in residential aged care facilities, causing significant health and economic burdens. However, the likelihood of future falls can be predicted, and thus, falls can be prevented if appropriate prevention programs are implemented. Current fall prevention programs in residential aged care facilities rely on risk screening tools with suboptimal predictive performance, leading to significant concerns regarding resident safety.</p><p><strong>Objective: </strong>This study aimed to develop a predictive, dynamic dashboard to identify residents at risk of falls with associated decision support. This paper provides an overview of the technical process, including the challenges faced and the strategies used to overcome them during the development of the dashboard.</p><p><strong>Methods: </strong>A predictive dashboard was co-designed with a major residential aged care partner in New South Wales, Australia. Data from resident profiles, daily medications, fall incidents, and fall risk assessments were used. A dynamic fall risk prediction model and personalized rule-based fall prevention recommendations were embedded in the dashboard. The data ingestion process into the dashboard was designed to mitigate the impact of underlying data system changes. This approach aims to ensure resilience against alterations in the data systems.</p><p><strong>Results: </strong>The dashboard was developed using Microsoft Power BI and advanced R programming by linking data silos. It includes dashboard views for those managing facilities and for those caring for residents. Data drill-through functionality was used to navigate through different dashboard views. Resident-level change in daily risk of falling and risk factors and timely evidence-based recommendations were output to prevent falls and enhance prescriptive decision support.</p><p><strong>Conclusions: </strong>This study emphasizes the significance of a sustainable dashboard architecture and how to overcome the challenges faced when developing a dashboard amid underlying data system changes. The development process used an iterative dashboard co-design process, ensuring the successful implementation of knowledge into practice. Future research will focus on the implementation and evaluation of the dashboard's impact on health processes and economic outcomes.</p><p><strong>International registered report identifier (irrid): </strong>RR2-https://doi.org/10.1136/bmjopen-2021-048657.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e63609"},"PeriodicalIF":5.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting Sleep/Wake Rhythm Disruption Related to Cognition in Older Adults With and Without Mild Cognitive Impairment Using the myRhythmWatch Platform: Feasibility and Correlation Study.","authors":"Caleb D Jones, Rachel Wasilko, Gehui Zhang, Katie L Stone, Swathi Gujral, Juleen Rodakowski, Stephen F Smagula","doi":"10.2196/67294","DOIUrl":"10.2196/67294","url":null,"abstract":"<p><strong>Background: </strong>Consumer wearable devices could, in theory, provide sufficient accelerometer data for measuring the 24-hour sleep/wake risk factors for dementia that have been identified in prior research. To our knowledge, no prior study in older adults has demonstrated the feasibility and acceptability of accessing sufficient consumer wearable accelerometer data to compute 24-hour sleep/wake rhythm measures.</p><p><strong>Objective: </strong>We aimed to establish the feasibility of characterizing 24-hour sleep/wake rhythm measures using accelerometer data gathered from the Apple Watch in older adults with and without mild cognitive impairment (MCI), and to examine correlations of these sleep/wake rhythm measures with neuropsychological test performance.</p><p><strong>Methods: </strong>Of the 40 adults enrolled (mean [SD] age 67.2 [8.4] years; 72.5% female), 19 had MCI and 21 had no cognitive disorder (NCD). Participants were provided devices, oriented to the study software (myRhythmWatch or myRW), and asked to use the system for a week. The primary feasibility outcome was whether participants collected enough data to assess 24-hour sleep/wake rhythm measures (ie, ≥3 valid continuous days). We extracted standard nonparametric and extended-cosine based sleep/wake rhythm metrics. Neuropsychological tests gauged immediate and delayed memory (Hopkins Verbal Learning Test) as well as processing speed and set-shifting (Oral Trails Parts A and B).</p><p><strong>Results: </strong>All participants meet the primary feasibility outcome of providing sufficient data (≥3 valid days) for sleep/wake rhythm measures. The mean (SD) recording length was somewhat shorter in the MCI group at 6.6 (1.2) days compared with the NCD group at 7.2 (0.6) days. Later activity onset times were associated with worse delayed memory performance (β=-.28). More fragmented rhythms were associated with worse processing speed (β=.40).</p><p><strong>Conclusions: </strong>Using the Apple Watch-based myRW system to gather raw accelerometer data is feasible in older adults with and without MCI. Sleep/wake rhythms variables generated from this system correlated with cognitive function, suggesting future studies can use this approach to evaluate novel, scalable, risk factor characterization and targeted therapy approaches.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e67294"},"PeriodicalIF":5.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11996143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Unmet Needs of Informal Dementia Caregivers in Clinical Practice: User-Centered Development of a Digital Assessment Tool.","authors":"Olga A Biernetzky, Jochen René Thyrian, Melanie Boekholt, Matthias Berndt, Wolfgang Hoffmann, Stefan J Teipel, Ingo Kilimann","doi":"10.2196/59942","DOIUrl":"10.2196/59942","url":null,"abstract":"<p><strong>Background: </strong>Despite the increasing interventions to support family caregivers of people with dementia, service planning and delivery is still not effective.</p><p><strong>Objective: </strong>Our study aimed to develop a digitally-supported needs assessment tool for family caregivers of people with dementia that is feasible, time-efficient, understood by users, and can be self-completed in the primary care setting.</p><p><strong>Methods: </strong>The development of the unmet needs assessment tool was part of a cluster-randomized controlled trial examining the effectiveness of a digitally supported care management programme to reduce unmet needs of family caregivers of people with dementia (GAIN [Gesund Angehörige Pflegen]) and was conducted in 3 phases. Using an iterative participatory approach with informal caregivers, health care professionals including general practitioners, neurologists, psychologists, psychiatrists, nurses, and Alzheimer Society representatives, we developed a digital self-completion unmet needs assessment tool focusing on informal caregivers' biopsychosocial health und quality of life in connection to their caregiver responsibilities. Data were collected through group discussions, written feedback, protocols, think-aloud protocols, and interviews, and analyzed thematically.</p><p><strong>Results: </strong>Data from 27 caregivers, including caregivers of people with dementia (n=18), health care professionals (n=7), and Alzheimer Society representatives (n=2) were collected. Thematic analysis identified 2 main themes: content of the assessment tool and usability and handling of the digital tablet-based assessment tool. The feedback provided by the stakeholders led to new aspects and changes to make the tool comprehensive, easy to read, and easy to handle. The overall mean completion time was reduced from the initial 37 minutes to 18 minutes, which renders the assessment tool fit to be self-completed in waiting rooms of primary care practices or other settings.</p><p><strong>Conclusions: </strong>The input of the 3 stakeholder groups has supported the development of the assessment tool ensuring that all aspects considered important were covered and understood and the completion of the assessment procedure was time-efficient and practically feasible. Further validation of the assessment tool will be performed with the data generated as part of the GAIN trial.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e59942"},"PeriodicalIF":5.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11996146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Older Adults' Perspectives on Participating in a Synchronous Online Exercise Program: Qualitative Study.","authors":"Giulia Coletta, Kenneth S Noguchi, Kayla Beaudoin, Angelica McQuarrie, Ada Tang, Rebecca Ganann, Stuart M Phillips, Meridith Griffin","doi":"10.2196/66473","DOIUrl":"10.2196/66473","url":null,"abstract":"<p><strong>Background: </strong>Older adults face several barriers to exercise participation, including transportation, lack of access, and poor weather conditions. Such barriers may influence whether older adults meet the Canadian 24-Hour Movement Guidelines. Recently, older adults have adopted technology for health care and are increasingly using digital health technologies to improve their access to care. Therefore, technology may be a valuable tool to reduce barriers to exercise and increase exercise participation rates within this population.</p><p><strong>Objective: </strong>This study aimed to explore older adults' perceptions and experiences of exercise, in general, and specifically related to our synchronous online exercise program for community-dwelling older adults.</p><p><strong>Methods: </strong>A total of 3 registered kinesiologists and 1 physiotherapist with experience working with older adults delivered an 8-week, thrice-weekly synchronous online group-based exercise program for older adults in 3 cohorts. The program focused on strength, balance, and aerobic activity. Following the program, a qualitative study with interpretive descriptive design was conducted to explore participants' perceptions and experiences. Participants were invited to take part in a 30-minute, one-on-one semistructured interview via Zoom with a research team member. Interview data were thematically analyzed to identify common themes.</p><p><strong>Results: </strong>A total of 22 older adults (16 women, 6 men; mean age 70, SD 4 years) participated in interviews. Three themes were identified as follows: (1) health, exercise, and aging beliefs; (2) the pandemic interruption and impacts; and (3) synchronous online exercise programs attenuate barriers to exercise. Participants discussed their exercise beliefs and behaviors and their desire to safely and correctly participate in exercise. Older adults found that their physical activity was curtailed, routines disrupted, and access to in-person exercise programs revoked due to the pandemic. However, many suggested that our synchronous online exercise program was motivational and attenuated commonly reported environmental barriers to participation, such as transportation concerns (eg, time spent traveling, driving, and parking), accessibility and convenience by participating at a location of their choice, and removing travel-related concerns during poor weather conditions.</p><p><strong>Conclusions: </strong>Given these reported experiences, we posit that synchronous online exercise programs may help motivate and maintain adherence to exercise programs for older adults. These findings may be leveraged to improve health outcomes in community-dwelling older adults.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e66473"},"PeriodicalIF":5.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11984789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Digitally Capable Aged Care Workforce: Demands and Directions for Workforce Education and Development.","authors":"Kathleen Gray, Kerryn Butler-Henderson, Karen Day","doi":"10.2196/54143","DOIUrl":"10.2196/54143","url":null,"abstract":"<p><p>As the aged care sector undergoes digital transformation, greater attention is needed to development of digital health capability in its workforce. There are many gaps in our understanding of the current and future impacts of technology on those who perform paid and unpaid aged care work. Research is needed to understand how to make optimal use of both digital resources and human resources for better aged care. In this Viewpoint, we reflect on a workshop held during an international conference that identified shared concepts and concerns to shape further research into workforce capability. Digital technologies and digital data can increase quality of care in a system that operates through partnerships among service providers, service users, and community members. To realize this potential, digital health learning and development are needed in the aged care workforce. As digital dimensions of aged care services expand, the sector needs clearer direction to implement approaches to workforce learning and development. These must be appropriate to support the safe and ethical performance of care work and to increase the satisfaction of those who care and those for whom they care.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e54143"},"PeriodicalIF":5.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study.","authors":"Natthanaphop Isaradech, Wachiranun Sirikul, Nida Buawangpong, Penprapa Siviroj, Amornphat Kitro","doi":"10.2196/62942","DOIUrl":"https://doi.org/10.2196/62942","url":null,"abstract":"<p><strong>Background: </strong>Frailty is defined as a clinical state of increased vulnerability due to the age-associated decline of an individual's physical function resulting in increased morbidity and mortality when exposed to acute stressors. Early identification and management can reverse individuals with frailty to being robust once more. However, we found no integration of machine learning (ML) tools and frailty screening and surveillance studies in Thailand despite the abundance of evidence of frailty assessment using ML globally and in Asia.</p><p><strong>Objective: </strong>We propose an approach for early diagnosis of frailty in community-dwelling older individuals in Thailand using an ML model generated from individual characteristics and anthropometric data.</p><p><strong>Methods: </strong>Datasets including 2692 community-dwelling Thai older adults in Lampang from 2016 and 2017 were used for model development and internal validation. The derived models were externally validated with a dataset of community-dwelling older adults in Chiang Mai from 2021. The ML algorithms implemented in this study include the k-nearest neighbors algorithm, random forest ML algorithms, multilayer perceptron artificial neural network, logistic regression models, gradient boosting classifier, and linear support vector machine classifier.</p><p><strong>Results: </strong>Logistic regression showed the best overall discrimination performance with a mean area under the receiver operating characteristic curve of 0.81 (95% CI 0.75-0.86) in the internal validation dataset and 0.75 (95% CI 0.71-0.78) in the external validation dataset. The model was also well-calibrated to the expected probability of the external validation dataset.</p><p><strong>Conclusions: </strong>Our findings showed that our models have the potential to be utilized as a screening tool using simple, accessible demographic and explainable clinical variables in Thai community-dwelling older persons to identify individuals with frailty who require early intervention to become physically robust.</p>","PeriodicalId":36245,"journal":{"name":"JMIR Aging","volume":"8 ","pages":"e62942"},"PeriodicalIF":5.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12038762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144031737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}