Geriatric Assessment

IntroductionGlobal ageing populations require accessible, non-invasive tools for early detection and monitoring of neurological chronic and neurodegenerative diseases. Current diagnostic methods face limitations including invasiveness, high costs and infrequent clinical assessments. The human voice has emerged as a promising digital biomarker, with vocal characteristics reflecting physiological and cognitive changes associated with conditions like dementia and Parkinson's disease. While artificial intelligence (AI) and machine learning have enabled sophisticated vocal analysis, the literature remains fragmented without comprehensive synthesis. This scoping review protocol delineates a systematic approach to collate and synthesise existing research on the application of AI-driven audio biomarkers for the detection and management of neurological diseases (eg, cognitive decline, Parkinson's disease, Alzheimer's, dementia and depression) in older adults aged 65 years and above.Methods and analysisThis scoping review will be conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and the methodological framework proposed by Arksey and O'Malley, incorporating recent methodological advancements. The eligibility criteria for study selection will be formulated using the PCC (Population, Concept, Context) framework. A comprehensive literature search will be performed across several electronic databases, including PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, Embase, Compendex, CINAHL, Scientific Information Database (SID), Magiran, IranMedex and Barakat Knowledge Network System (BKNS). The search will encompass peer-reviewed articles published in Persian and English from 1 January 2012 to 31 March 2026. Two independent reviewers will screen titles, abstracts and full texts and extract data according to the predefined PCC-based eligibility criteria. Discrepancies will be resolved through discussion or, if necessary, by consultation with a third reviewer. The results will be synthesised and presented narratively, accompanied by summary tables, charts and figures to address each research question.Ethics and disseminationThe Research Ethics Committee of Tabriz University of Medical Sciences approved the protocol for this scoping review (approval number: IR.TBZMED.VCR.REC.1404.223). They concluded that since the review involves only analysis of existing literature without direct patient involvement or clinical procedures, it meets the relevant ethical standards. Results from the review will be shared through peer-reviewed journals and conference presentations to provide valuable insights for researchers, clinicians and policymakers on the use of audio-based biomarkers in older adults.Prospero registration numberNot registered.

BackgroundThis study aimed to investigate the prevalence and influencing factors of oral frailty, a significant geriatric syndrome, among hospitalized older adults with chronic diseases, and to develop a corresponding risk prediction model to facilitate early screening and intervention.MethodsFrom September 2024 to May 2025, we recruited 443 older adult patients with chronic diseases from two tertiary grade A hospitals in Wuhu City, Anhui Province, using a convenience sampling method. Data were collected using a general information questionnaire, the Oral Frailty Index-8 (OFI-8), the FRAIL scale, the Appetite Scale, and the Clinical Physiological Resilience Scale. Binary logistic regression analysis was conducted to identify factors associated with oral frailty.ResultsThe prevalence of oral frailty among hospitalized older adult patients with chronic diseases was 69.3%(307/443). Appetite, employment status, age, Clinical physiological resilience, and frailty were the best predictors of oral frailty in hospitalized older adult patients with chronic diseases. These influencing factors were utilized to construct a nomogram model, which demonstrated excellent consistency and accuracy. The area under the curve (AUC) in the training set was 0.805 (95% confidence interval [CI] = 0.753-0.857). In the validation set, the AUC was 0.900 (95% CI = 0.843-0.957). The Hosmer-Lemeshow test values were p = 0.465 and p = 0.161 (both > 0.05). The calibration curve indicated a significant agreement between the nomogram model and the actual observations. Both the ROC curve and decision curve analysis (DCA) demonstrated that the nomogram possesses outstanding risk predictive performance.ConclusionThe prevalence of oral frailty is notably high among hospitalized older adult patients with chronic diseases, with key influencing factors including age, employment status, appetite, and clinical physiological resilience. The developed risk prediction model demonstrates good calibration and discriminative ability, supporting its potential use in early screening and targeted intervention of oral frailty in this population.

AimTo explore the perceptions of elderly patients with chronic diseases and their healthcare providers on the use of wearable devices for home health monitoring, identifying barriers and facilitators to their effective adoption in China's rapidly aging population.BackgroundAs China's population ages, managing chronic diseases among elderly patients has become increasingly complex. Wearable health monitoring devices offer a promising solution, enabling remote and continuous health tracking. However, the adoption of these devices remains limited, particularly among elderly patients. Understanding the perspectives of both patients and healthcare providers is crucial for optimizing wearable device use in chronic disease management.DesignA qualitative descriptive study.MethodFrom May 2023 to March 2024, semistructured interviews were conducted with 16 elderly patients and 11 healthcare providers in Nanjing, China. Participants were selected through purposive sampling. Data were analyzed using Colaizzi's 7-step method, generating key themes that address both the challenges and opportunities in adopting wearable health technologies.FindingsSeven major themes emerged: (1) Technology Acceptance and Motivation, (2) Changes in Social Support and Interaction, (3) Adjustments in Healthcare Work Modes, (4) Barriers and Risks in Technology Application, (5) Device Compliance and Knowledge Gaps, (6) Identifying Key Features for Quality Assessment of Wearable Devices, and (7) The Role of Customization and Adaptability. While the potential benefits of wearable devices for chronic disease management were widely recognized, concerns about complexity, cost, and data security were key obstacles.ConclusionWearable devices hold significant potential for improving the management of chronic diseases among elderly patients, yet multiple barriers hinder their widespread adoption. Addressing issues related to usability, privacy, and affordability, alongside providing education and policy support, will be critical to enhancing their integration into healthcare settings.Implications for the profession and patient careThis study underscores the importance of creating targeted strategies to overcome the challenges in using wearable health devices among elderly patients. Healthcare providers and policymakers should focus on simplifying technology, enhancing patient education, and addressing privacy concerns to foster broader acceptance and use of these devices in chronic disease care.ImpactBy promoting the adoption of wearable health technologies, healthcare systems can improve chronic disease management outcomes for elderly patients, reduce the burden on healthcare services, and support a more patient-centered approach to care.Patient or public contributionElderly patients with chronic diseases and their healthcare providers contributed valuable insights by sharing their experiences with wearable health monitoring devices, shaping the study's findings.