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Increasing demand on health care systems requires innovative, transformative solutions for efficient, high-quality care. One promising approach is digital twin (DT) technology, which leverages real-time data to create dynamic, virtual representations of a physical entity (individuals or space) to anticipate future scenarios and support care decisions. Although DTs have been explored in various sectors, their application in hospital at home (HaH), which delivers acute-level care in home environments, remains unexplored. This review bridges a critical knowledge gap, examining existing evidence on DT-enabling tools to manage patients with frailty in home settings. This will identify the underpinning architectural components required to inform an HaH-DT system supporting clinical decision-making. We searched 6 electronic databases and gray literature for primary English-language studies published between January 2019 and September 2025. Included studies reported on the monitoring or management of patients with frailty within their own home. Information was charted on a predefined data collection form to answer the research objectives. Review articles, protocols, and conference abstracts were excluded. We included 69 reports: 54% (37/69) used quantitative approaches, and 36% (25/69) were pilot or feasibility studies. Reports were analyzed for DT-enabling tools and systematically mapped across the proposed 5-layered DT architecture: sensing, communication, storage, analytics, and visualization. DT layer taxonomies, interconnections, and classifications of data types collected (eg, about the patient, home environment, use of medical equipment) are presented. This evidence identifies DT-enabling tools for a variety of functions and a range of sensing technologies (eg, wearable-based passive sensing, active physiological sensors, ambient sensors detecting motion or environmental changes). The most prevalent communication modes were wireless and network-based (36/112, 32.1%), the majority (12/36, 33%) using Bluetooth. Better understanding of data management, particularly secure storage, is required within local health care systems. The emerging potential of predictive and prescriptive analytics for risk prediction, clinical decision-making, or activation of alert-triggered health interventions by clinicians was mapped. Analytics methods are currently largely descriptive. Advanced methods such as prescriptive analytics for recommendations of an optimal course of action and diagnostic analytics that highlight why a situation has occurred are lacking. DT-enabling tools demonstrate patient-centered benefits, including enhanced motivation, reassurance, and personalized care. Concerns include device accuracy, user acceptability, and implications for carers and organizational workflows. This review is among the first to systematically map DT-enabling tools to inform a potential HaH DT for patients with frailty, organized by a 5-layered conceptual model. Understanding these architectural layers provides the foundations for stakeholders to advance research and development in areas where there are knowledge gaps and consider how an HaH DT can effectively operate within current health care systems. Leveraging technology-enabled care in complex home-based settings provides great potential to deliver safer, personalized, timely care.

Frailty and sarcopenia represent substantial global health challenges, frequently diminishing patients' quality of life through impaired muscle function and physical performance. Digital health interventions (DHIs) have shown promise in mitigating these conditions among older adults. However, outcomes of such interventions in this demographic are inconsistent, and a thorough synthesis of existing evidence is lacking. This study aimed to evaluate the effectiveness of DHIs in older adults with frailty and sarcopenia. A comprehensive search of PubMed, Web of Science, MEDLINE, Embase, and Cochrane Library was conducted from their inception until January 2026 to identify randomized controlled trials. Meta-analyses were performed using R software (R Foundation for Statistical Computing). Study quality was evaluated using the revised Cochrane Risk of Bias Tool 2.0 (Cochrane Collaboration), and evidence certainty was assessed using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). From 3506 records, 16 studies were included. DHIs significantly improved total skeletal muscle mass (weighted mean difference [WMD] 1.01, 95% CI 0.08-1.94, 95% prediction interval [PI] -0.95 to 2.96), gait speed (WMD 0.09, 95% CI 0.03-0.15, 95% PI -0.1 to 0.26), Timed Up and Go Test (TUGT: WMD -0.52, 95% CI -1.02 to -0.03, 95% PI -1.93 to 0.85), 30-second Chair Stand Test (30CST: WMD 2.19, 95% CI 0.89-3.48, 95% PI -1.59 to 5.66), balance (standardized mean difference [SMD] 0.61, 95% CI 0-1.21, 95% PI -0.94 to 2.13), and quality of life (SMD 0.16, 95% CI 0.05-0.27, 95% PI 0.04-0.28). No significant improvements were observed in Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-minute walk test (6MWT), 2-minute walk test (2MWT), Short Physical Performance Battery (SPPB), or BMI. Although the pooled effect was favorable, the wide 95% PI suggests substantial between-study heterogeneity. Subgroup analysis stratified by intervention duration revealed significant intersubgroup differences in ASMI (χ²₁=9.93; P=.0016), indicating interventions lasting ≥12 weeks were more effective for improving ASMI (WMD 0.28, 95% CI 0.06-0.50, 95% PI -0.30 to 0.83). Subgroup analysis stratified by intervention type showed significant intersubgroup differences in balance (χ²₃=9.89; P=.0195), with exergame-based interventions showing significant effects (SMD 0.83, 95% CI 0.26-1.40). This systematic review is the first to quantify the disease-specific efficacy of DHIs in improving muscle function, physical performance, and quality of life among older adults with frailty and sarcopenia, demonstrating their unique value as a scalable complementary approach. By overcoming geographical and resource constraints, DHIs support underserved populations. However, low evidence quality and heterogeneity warrant cautious interpretation. The 95% PIs indicate that actual effects may vary with population characteristics and implementation contexts. Nonetheless, DHIs represent a promising and cost-effective strategy for service expansion. Future high-quality studies are needed to better understand their effectiveness and implementation across settings.

Remote monitoring is increasingly used in heart failure care, but most programs rely on uniform models that insufficiently reflect the heterogeneity of older adults, particularly with respect to frailty, cognitive impairment, functional dependency, and caregiver availability. This viewpoint argues that frailty should be considered a central determinant of remote monitoring design in older adults with heart failure, rather than a secondary modifier of conventional digital health pathways. Drawing on evidence from heart failure telemonitoring, geriatric medicine, and real-world cardiogeriatric experience, we propose a frailty-adaptive framework structured around four clinical trajectories: robust, prefrail, frail, and palliative. Each trajectory is associated with distinct monitoring objectives, workflow adaptations, and response pathways. Robust patients may benefit primarily from optimization, self-management support, and guideline-directed therapy titration; prefrail patients from early detection of deterioration and functional decline; frail patients from proxy-supported reporting, nurse-led triage, and rapid-access cardiogeriatric reassessment; and palliative patients from simplified symptom-guided monitoring focused on comfort, hospitalization avoidance, and caregiver support. This framework reframes remote monitoring as a stratified clinical process rather than a purely technological intervention. By aligning digital strategies with frailty status, functional capacity, and care goals, frailty-adaptive remote monitoring may improve clinical relevance, promote digital health equity, and support more sustainable models of care for older adults with heart failure.

Frailty constitutes a growing challenge for health and social care systems around the world. In England, 35% of adults aged 65 years and older live with frailty, with international estimates indicating that almost half of all hospital inpatients within the same age group are frail. This population often experiences multimorbidity and frequent care transitions. Written documentation and verbal handovers may lack the precision and nuance required to understand an older adult's presentation and support needs. Video recordings of individual patients, capturing aspects of their functional abilities and condition, may help to enhance multidisciplinary team communication and care continuity, yet little is known about their use in the care of older inpatients with frailty. We aimed to evaluate the acceptability, feasibility of implementation, and perceived effectiveness of video-based patient records (the Isla Health Digital Pathway Platform) for supporting the assessment and care of older inpatients with frailty within the acute hospital setting. A nonrandomized mixed methods pilot study was conducted within 3 acute medicine wards for older adults. The video-based patient records intervention, permitting videos to be embedded securely within the electronic patient record, was implemented over a 3-month period alongside usual care. Patient enrollment and retention figures; qualitative interviews with patients, carers, and clinical staff; and video capture and view metrics were used to address the study objectives. The Theoretical Framework of Acceptability of Healthcare Interventions was applied to the framework analysis of interview data, capturing concepts such as intervention ethicality, burden, and coherence. Patient and public involvement and engagement informed each research stage. Twenty-nine patients were enrolled (56.9%); 1 patient withdrew before receiving the intervention. Modal reasons given by patients for nonparticipation included not wanting to take part in research (n=8) or feeling too unwell (n=2). Staff identified multiple opportunities for capturing patient videos, including documentation of mobility assessments or seizures. The intervention was considered acceptable on the grounds that safeguards were always in place, including secure data storage and upholding of patient dignity. Implementation barriers and facilitators were identified; factors such as difficulties in capturing videos within busy ward environments and scheduling issues were voiced by participants. Video view metrics and data from interviews collectively suggested low rates of engagement with videos by clinical staff once captured. Potential intervention impacts included perceived enhancements to clinical assessment and person-centered care. Our findings suggest that the intervention is largely acceptable to patients, carers, and clinical staff. Conclusions as to intervention feasibility were mixed, with limited engagement with videos suggesting further work is required to promote sufficient uptake among staff. Finally, this research presents promising patient, carer, and clinical opinion as to the potential effectiveness of video-based patient records for improving aspects of patient care.

Chemotherapy causes physiological, psychological, and social impairments in patients with cancer. Frailty reduces the effectiveness of chemotherapy and increases the toxicity associated with radiotherapy and chemotherapy, the possibility of chemotherapy failure, and adverse outcomes. However, factors affecting chemotherapy-related frailty in patients with cancer remain unclarified. This systematic review aimed to identify risk factors driving frailty progression during chemotherapy in patients with cancer. A comprehensive systematic search was conducted on PubMed, Web of Science, Embase, China National Knowledge Infrastructure, China Science and Technology Journal Database (VIP), and SinoMed for observational studies (cohort, cross-sectional, or case-control) on factors affecting the debility-of-chemotherapy stage in patients with cancers between the inception of the database and February 2025, with an updated search executed in May 2025. Literature screening, quality evaluation using the Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality checklist, and data extraction were conducted independently by 2 authors. Meta-analysis, effect size combination, sensitivity analysis, and publication bias analysis were performed using RevMan (version 5.4; The Cochrane Collaboration) and R (version 4.4.3; R Foundation). The analysis comprised 14 studies (8 cross-sectional, 2 repeated cross-sectional, 3 cohort, and 1 mixed-design), including 3879 patients with cancer and 23 influencing factors. Methodological quality assessment using Agency for Healthcare Research and Quality (mean 8.8, SD 1.3, 95% CI 7.9-9.7; SE 0.4) and Newcastle-Ottawa Scale (mean 8.0, SD 1.0, 95% CI 6.7-9.3; SE 0.6) revealed 73% (8/11) of cross-sectional studies as high-quality. The meta-analysis showed a 35% (95% CI 22%-50%) prevalence of frailty during chemotherapy in these patients. Cancer stage (odds ratio 1.99, 95% CI 1.64-2.42), chemotherapy frequency (odds ratio 2.60, 95% CI 1.83-3.70), transfer (odds ratio 2.18, 95% CI 1.50-3.17), hemoglobin (odds ratio 0.29, 95% CI 0.18-0.47), white blood cell (odds ratio 0.37, 95% CI 0.21-0.65), comorbidity (odds ratio 1.93, 95% CI 1.30-2.86), and hypoproteinemia (odds ratio 1.74, 95% CI 1.31-2.30) were risk factors for frailty in patients at the chemotherapy stage. Frailty during chemotherapy was strongly associated with advanced cancer stage, frequent treatment cycles, metastasis, anemia, leukopenia, comorbidities, and hypoproteinemia. Clinically actionable findings emphasized hemoglobin and albumin monitoring as preventive targets, while heterogeneity in assessment tools and population bias limited generalizability. The integration of frailty screening into chemotherapy workflows is urgent to mitigate treatment-related functional decline.

Disturbed sleep patterns are common among older adults and may contribute to cognitive and physical declines. However, evidence for the relationship between sleep duration and cognitive frailty, a concept combining physical frailty and cognitive impairment in older adults is lacking. The objective of our study was to examine the associations of sleep duration and its changes with cognitive frailty. We analyzed data from the 2008-2018 waves of the Chinese Longitudinal Healthy Longevity Survey. Cognitive frailty was rendered based on the modified Fried frailty phenotype and Mini-Mental State Examination. Sleep duration was categorized as short (<6 h), moderate (6-9 h), and long (>9 h). We examined the association of sleep duration with cognitive frailty status at baseline using logistic regressions and with future incidence of cognitive frailty using Cox proportional hazards models. Restricted cubic splines were employed to explore potential non-linear associations. Among 11,303 participants, 1,298 (11.5%) had cognitive frailty at baseline. Compared to participants who had moderate sleep duration, the odds of having cognitive frailty were higher in those with long sleep duration (odds ratio [OR] =1.71, 95% confidence interval [CI] =1.48-1.97, p<0.001). A J-shaped association between sleep duration and cognitive frailty was also observed (p<0.001). Additionally, during a median follow-up of 6.7 years among 5,201 participants who were not cognitively frail at baseline, 521 (10.0%) developed cognitive frailty. A higher risk of cognitive frailty was observed in participants with long sleep duration (hazard ratio [HR] =1.32, 95% CI =1.07-1.62, p=0.008). Long sleep duration was associated with cognitive frailly in older Chinese adults. These findings provide insights into the relationship between sleep duration and cognitive frailty, with potential implications for public health policies and clinical practice.

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. 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. 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. 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. 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.

With the advent of antiretroviral therapy, the life expectancy of people living with HIV has increased significantly, leading to a growing prevalence of frailty and its associated adverse outcomes. However, frailty prediction models developed for the general older population may not apply to people living with HIV due to their distinct immunologic, inflammatory, and comorbidity profiles. To the best of our knowledge, no systematic review to date has comprehensively evaluated frailty prediction models specifically developed for people living with HIV. This systematic review aims to identify and critically appraise existing prediction models for frailty in people living with HIV, critically appraise their methodological quality, and summarize their predictive performance. Research will be located by searching electronic databases, including PubMed, Web of Science, and other major databases. Two independent reviewers will conduct the screening of titles and abstracts, evaluate full texts, and extract data. The extraction process will adhere to the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD) statement. A systematic evaluation of the included studies will be performed to assess their risk of bias and applicability, using the Prediction model Risk Of Bias Assessment Tool (PROBAST). If appropriate, meta-analyses will be used to synthesize quantitative data related to the predictive performance of these models. This protocol was registered with PROSPERO (CRD420261332573) in June 2025. The systematic literature search was conducted on July 20, 2025, with screening completed in October 2025, identifying five eligible studies; data analysis is scheduled for April 2026, with manuscript submission expected by June 2026. This systematic review will provide the first comprehensive synthesis of frailty prediction models for people living with HIV. By identifying robust models and methodological gaps, the findings are expected to inform clinical decision-making and guide future model development and validation efforts in HIV care.

The aging trend of people living with HIV or AIDS in China is increasing day by day. Frailty is a common condition among older adults living with HIV or AIDS and represents a significant cause of poor prognosis, including falls, decreased quality of life, increased mortality, and potentially prolonged hospital stays. Consequently, early frailty screening in this population holds important clinical significance. This study aims to describe the theoretical basis, research objectives, and implementation plan of a prospective observational study. It will focus on investigating the current status of frailty syndrome in hospitalized older adults living with HIV or AIDS, while simultaneously exploring the development of a clinically applicable risk prediction model. This study is an ongoing single-center prospective observational study, with a plan to recruit at least 556 hospitalized older adults living with HIV or AIDS (n=445 for development and n=111 for validation). According to the theory of unpleasant symptoms, candidate predictors are categorized into physiological factors (including sociodemographic factors, disease-related influencing factors, sleep, nutrition, and neurocognitive function), psychological factors (including anxiety and depression status), and environmental factors (including social support status). Potential predictors are screened using univariate analysis and least absolute shrinkage and selection operator regression to identify variables for final model inclusion. Model construction and validation employ 3 standard machine learning algorithms: logistic regression, random forest, and support vector machine. Model performance will be evaluated by reporting accuracy, precision, sensitivity, specificity, and the area under the curve. This study is conducted at a designated infectious disease hospital in Changsha, Hunan Province, China. Participant recruitment commenced on December 22, 2024, and as of December 5, 2025, a total of 603 patients have been enrolled. The primary study findings are anticipated to be published in August 2026. The findings of this study are expected to provide clinicians in the department of infectious diseases with a convenient tool for frailty risk prediction, thereby enabling early intervention and ultimately improving the long-term health status and quality of life of people living with HIV.

Postoperative frailty is highly prevalent among older adults undergoing hip surgery and is closely linked to poor clinical outcomes. Despite growing interest in understanding its progression, the temporal patterns of frailty remain underexplored. Moreover, there is a lack of validated models that can predict frailty trajectories and stratify patients by risk in the early postoperative period. This study aimed to identify distinct frailty trajectories within 6 months following hip surgery in older adults and to explore their associated predictors. An interpretable machine-learning model was developed and internally validated for individualized risk prediction and was implemented as a clinically accessible web-based calculator. This prospective longitudinal observational study was conducted among older adults undergoing hip surgery at a tertiary hospital in China. Frailty assessments were performed preoperatively and at 1, 3, and 6 months postoperatively. A total of 209 participants who completed the 6-month follow-up were included in the analysis. Frailty was assessed using the Frailty Index, and group-based trajectory modeling was applied to identify distinct frailty progression patterns. Predictive variables were selected using the least absolute shrinkage and selection operator regression. An interpretable Extreme Gradient Boosting (XGBoost) model was developed using a 60:40 training-test data split. Model performance was evaluated in terms of discrimination, calibration, and clinical utility. Interpretability was assessed using SHAP (Shapley Additive Explanations) at both the global and individual levels. Three distinct frailty trajectories were identified: low-fluctuation frailty (55/209, 26%), high-improvement frailty (81/209, 39%), and high-deterioration frailty (73/209, 35%). Twelve predictors grounded in the Health Ecology Model were selected, spanning individual characteristics, interpersonal networks, and the living environment. The XGBoost model demonstrated excellent discrimination, with a microaverage area under the receiver operating characteristic curve of 0.98 (95% CI 0.96-0.99) in the training set and 0.93 (95% CI 0.90-0.96) in the test set. Calibration was acceptable, with a weighted Brier score of 0.0852. Decision curve analysis showed favorable clinical utility across a range of threshold probabilities. A web-based risk calculator was developed to facilitate personalized frailty trajectory prediction. The XGBoost model demonstrated strong predictive performance and interpretability, enabling the early identification of older patients at risk for adverse frailty trajectories following hip surgery. This tool may support targeted interventions and improve perioperative care in geriatric populations.