%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e65551 %T Current Technological Advances in Dysphagia Screening: Systematic Scoping Review %A Wong,Duo Wai-Chi %A Wang,Jiao %A Cheung,Sophia Ming-Yan %A Lai,Derek Ka-Hei %A Chiu,Armstrong Tat-San %A Pu,Dai %A Cheung,James Chung-Wai %A Kwok,Timothy Chi-Yui %+ Department of Biomedical Engineering, Faculty of Engineering, Hong Kong Polytechnic University, GH137, GH Wing, 1/F, Department of Biomedical Engineering,, 11 Yuk Choi Road, Hung Hom, Kowloon, Hong Kong, 999077, China (Hong Kong), 852 27667673, james.chungwai.cheung@polyu.edu.hk %K digital health %K computer-aided diagnosis %K computational deglutition %K machine learning %K deep learning %K artificial intelligence %K AI %K swallowing disorder %K aspiration %D 2025 %7 5.5.2025 %9 Review %J J Med Internet Res %G English %X Background: Dysphagia affects more than half of older adults with dementia and is associated with a 10-fold increase in mortality. The development of accessible, objective, and reliable screening tools is crucial for early detection and management. Objective: This systematic scoping review aimed to (1) examine the current state of the art in artificial intelligence (AI) and sensor-based technologies for dysphagia screening, (2) evaluate the performance of these AI-based screening tools, and (3) assess the methodological quality and rigor of studies on AI-based dysphagia screening tools. Methods: We conducted a systematic literature search across CINAHL, Embase, PubMed, and Web of Science from inception to July 4, 2024, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. In total, 2 independent researchers conducted the search, screening, and data extraction. Eligibility criteria included original studies using sensor-based instruments with AI to identify individuals with dysphagia or unsafe swallow events. We excluded studies on pediatric, infant, or postextubation dysphagia, as well as those using non–sensor-based assessments or diagnostic tools. We used a modified Quality Assessment of Diagnostic Accuracy Studies–2 tool to assess methodological quality, adding a “model” domain for AI-specific evaluation. Data were synthesized narratively. Results: This review included 24 studies involving 2979 participants (1717 with dysphagia and 1262 controls). In total, 75% (18/24) of the studies focused solely on per-individual classification rather than per–swallow event classification. Acoustic (13/24, 54%) and vibratory (9/24, 38%) signals were the primary modality sources. In total, 25% (6/24) of the studies used multimodal approaches, whereas 75% (18/24) used a single modality. Support vector machine was the most common AI model (15/24, 62%), with deep learning approaches emerging in recent years (3/24, 12%). Performance varied widely—accuracy ranged from 71.2% to 99%, area under the receiver operating characteristic curve ranged from 0.77 to 0.977, and sensitivity ranged from 63.6% to 100%. Multimodal systems generally outperformed unimodal systems. The methodological quality assessment revealed a risk of bias, particularly in patient selection (unclear in 18/24, 75% of the studies), index test (unclear in 23/24, 96% of the studies), and modeling (high risk in 13/24, 54% of the studies). Notably, no studies conducted external validation or domain adaptation testing, raising concerns about real-world applicability. Conclusions: This review provides a comprehensive overview of technological advancements in AI and sensor-based dysphagia screening. While these developments show promise for continuous long-term tele-swallowing assessments, significant methodological limitations were identified. Future studies can explore how each modality can target specific anatomical regions and manifestations of dysphagia. This detailed understanding of how different modalities address various aspects of dysphagia can significantly benefit multimodal systems, enabling them to better handle the multifaceted nature of dysphagia conditions. %M 40324167 %R 10.2196/65551 %U https://www.jmir.org/2025/1/e65551 %U https://doi.org/10.2196/65551 %U http://www.ncbi.nlm.nih.gov/pubmed/40324167 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 14 %N %P e65213 %T Multihealth Promotion Programs on Physical Health and Quality of Life in Older Adults: Quasi-Experimental Study %A Lee,Li-Yun %A Tung,Heng-Hsin %A Liao,George %A Liu,Su-Ju %A Chen,Zi-Yu %A Yang,Yea-Ru %+ , Department of Nursing, National Yang Ming Chiao Tung University, No 155, Sec 2, Linong St, Beitou Dist, Taipei, 112, Taiwan, 886 2 2826 7000 ext 67991, shannontung719@gmail.com %K older adult %K body composition %K physical activity %K health promotion %K exercise %K nutrition %K diet %K well-being %K quality-of-life %K QoL %K gerontology %K geriatrics %D 2025 %7 1.5.2025 %9 Original Paper %J Interact J Med Res %G English %X Background: Physical activity and appropriate nutrition are essential for older adults. Improving physical health and quality of life can lead to healthy aging. Objective: This study aims to investigate the long-term effects of multihealth promotion programs on the physical and mental health of older adults in communities. Methods: A quasi-experimental method was used to recruit 112 older adults voluntarily from a pharmacy in central Taiwan between April 2021 and February 2023. Participants were divided into an experimental group receiving a multihealth promotion program and a control group with no specific intervention. The study measured frailty, nutritional status, well-being, and quality of life using standardized tools such as the Clinical Frailty Scale (CFS), Mini-Nutritional Assessment-Short Form (MNA-SF), Well-being Scale for Elders, and the EQ-5D-3L. Data were analyzed using descriptive statistics, independent t tests, Pearson correlation, and generalized estimating equations. Results: A total of 112 participants were recruited. There were 64 (57.1%) in the experimental group and 48 (42.9%) in the control group. The experimental group exhibited significantly better quality of life (EQ-5D index) at weeks 12 (β=–.59; P=.01) and 24 (β=–.44; P=.04) compared to the control group. The experimental group muscle mass significantly increased at weeks 24 (β=4.29; P<.01) and 36 (β=3.03; P=.01). Upper limb strength improved significantly at weeks 12 (β=3.4; P=.04) and 36 (β=5; P=.01), while core strength showed significant gains at weeks 12 (β=4.43; P=.01) and 36 (β=6.99; P<.01). Lower limb strength increased significantly only at week 12 (β=4.15; P=.01). Overall physical performance improved significantly at weeks 12 (β=5.47; P<.01), 24 (β=5.17; P<.01), and 36 (β=8.79; P<.01). Conclusions: The study’s findings highlight the practical benefits of interventions, including physical and social activities and nutritional support, in enhancing the quality of life and general physical health of older adults. This study’s findings have significant implications for clinical practice. These findings can aid in the establishment of effective interventions for older adults. Trial Registration: ClinicalTrials.gov NCT05412251; https://clinicaltrials.gov/study/NCT05412251 %M 40310677 %R 10.2196/65213 %U https://www.i-jmr.org/2025/1/e65213 %U https://doi.org/10.2196/65213 %U http://www.ncbi.nlm.nih.gov/pubmed/40310677 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64473 %T Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study %A Jeong,Chang-Uk %A Leiby,Jacob S %A Kim,Dokyoon %A Choe,Eun Kyung %K biological age %K aging clock %K mortality %K artificial intelligence %K machine learning %K record %K history %K health checkup %K clinical relevance %K gerontology %K geriatric %K older %K elderly %K aging %K prediction %K predictive %K life expectancy %K AI %D 2025 %7 11.4.2025 %9 %J JMIR Aging %G English %X Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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. %R 10.2196/64473 %U https://aging.jmir.org/2025/1/e64473 %U https://doi.org/10.2196/64473 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e69504 %T Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study %A Socrates,Vimig %A Wright,Donald S %A Huang,Thomas %A Fereydooni,Soraya %A Dien,Christine %A Chi,Ling %A Albano,Jesse %A Patterson,Brian %A Sasidhar Kanaparthy,Naga %A Wright,Catherine X %A Loza,Andrew %A Chartash,David %A Iscoe,Mark %A Taylor,Richard Andrew %+ Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, 464 Congress Avenue, Suite 260, New Haven, CT, 06510, United States, 1 2037854058, richard.taylor@yale.edu %K deprescribing %K large language models %K geriatrics %K potentially inappropriate medication list %K emergency medicine %K natural language processing %K calibration %D 2025 %7 11.4.2025 %9 Original Paper %J JMIR Aging %G English %X Background: 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. Objective: 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. Methods: 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. Results: 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: χ21=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. Conclusions: This study highlights the potential of LLMs to support deprescribing decisions in the ED by effectively filtering relevant criteria. However, challenges remain in applying these criteria to complex clinical scenarios, as the LLM demonstrated poor performance on more intricate decision-making tasks, with its reported confidence often failing to align with its actual success in these cases. The findings underscore the need for clearer deprescribing guidelines, improved LLM calibration for real-world use, and better integration of human–artificial intelligence workflows to balance artificial intelligence recommendations with clinician judgment. %M 40215480 %R 10.2196/69504 %U https://aging.jmir.org/2025/1/e69504 %U https://doi.org/10.2196/69504 %U http://www.ncbi.nlm.nih.gov/pubmed/40215480 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e65629 %T Model-Based Feature Extraction and Classification for Parkinson Disease Screening Using Gait Analysis: Development and Validation Study %A Lim,Ming De %A Connie,Tee %A Goh,Michael Kah Ong %A Saedon,Nor ‘Izzati %+ Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka, 75450, Malaysia, 60 62523592, tee.connie@mmu.edu.my %K model-based features %K gait analysis %K Parkinson disease %K computer vision %K support vector machine %D 2025 %7 8.4.2025 %9 Original Paper %J JMIR Aging %G English %X Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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. %M 40198116 %R 10.2196/65629 %U https://aging.jmir.org/2025/1/e65629 %U https://doi.org/10.2196/65629 %U http://www.ncbi.nlm.nih.gov/pubmed/40198116 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e62942 %T Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study %A Isaradech,Natthanaphop %A Sirikul,Wachiranun %A Buawangpong,Nida %A Siviroj,Penprapa %A Kitro,Amornphat %K aged care %K gerontology %K geriatric %K old %K aging %K clinical decision support %K delivering health information and knowledge to the public %K diagnostic systems %K digital health %K epidemiology %K surveillance %K diagnosis %K frailty %K machine learning %K prediction %K predictive %K AI %K artificial intelligence %K Thailand %K community dwelling %K health care intervention %K patient care %D 2025 %7 2.4.2025 %9 %J JMIR Aging %G English %X Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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. %R 10.2196/62942 %U https://aging.jmir.org/2025/1/e62942 %U https://doi.org/10.2196/62942 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e63686 %T Using Deep Learning to Perform Automatic Quantitative Measurement of Masseter and Tongue Muscles in Persons With Dementia: Cross-Sectional Study %A Imani,Mahdi %A Borda,Miguel G %A Vogrin,Sara %A Meijering,Erik %A Aarsland,Dag %A Duque,Gustavo %K artificial intelligence %K machine learning %K sarcopenia %K dementia %K masseter muscle %K tongue muscle %K deep learning %K head %K tongue %K face %K magnetic resonance imaging %K MRI %K image %K imaging %K muscle %K muscles %K neural network %K aging %K gerontology %K older adults %K geriatrics %K older adult health %D 2025 %7 19.3.2025 %9 %J JMIR Aging %G English %X Background: Sarcopenia (loss of muscle mass and strength) increases adverse outcomes risk and contributes to cognitive decline in older adults. Accurate methods to quantify muscle mass and predict adverse outcomes, particularly in older persons with dementia, are still lacking. Objective: This study’s main objective was to assess the feasibility of using deep learning techniques for segmentation and quantification of musculoskeletal tissues in magnetic resonance imaging (MRI) scans of the head in patients with neurocognitive disorders. This study aimed to pave the way for using automated techniques for opportunistic detection of sarcopenia in patients with neurocognitive disorder. Methods: In a cross-sectional analysis of 53 participants, we used 7 U-Net-like deep learning models to segment 5 different tissues in head MRI images and used the Dice similarity coefficient and average symmetric surface distance as main assessment techniques to compare results. We also analyzed the relationship between BMI and muscle and fat volumes. Results: Our framework accurately quantified masseter and subcutaneous fat on the left and right sides of the head and tongue muscle (mean Dice similarity coefficient 92.4%). A significant correlation exists between the area and volume of tongue muscle, left masseter muscle, and BMI. Conclusions: Our study demonstrates the successful application of a deep learning model to quantify muscle volumes in head MRI in patients with neurocognitive disorders. This is a promising first step toward clinically applicable artificial intelligence and deep learning methods for estimating masseter and tongue muscle and predicting adverse outcomes in this population. %R 10.2196/63686 %U https://aging.jmir.org/2025/1/e63686 %U https://doi.org/10.2196/63686 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67715 %T Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study %A Nejadshamsi,Shayan %A Karami,Vania %A Ghourchian,Negar %A Armanfard,Narges %A Bergman,Howard %A Grad,Roland %A Wilchesky,Machelle %A Khanassov,Vladimir %A Vedel,Isabelle %A Abbasgholizadeh Rahimi,Samira %+ Family Medicine Department, Faculty of Medicine and Health Sciences, McGill University, 5858 Côte des Negies, Montreal, QC, H3S 1Z1, Canada, 1 5143987375, samira.rahimi@mcgill.ca %K depression %K classification %K machine learning %K artificial intelligence %K older adults %D 2025 %7 3.3.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection is vital for effective treatment and intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely on intrusive methods. Additionally, most depression classification studies involve large participant groups and use single-stage classifiers without explainability. Objective: This study aims to assess the feasibility of classifying depression using nonintrusive Wi-Fi–based motion sensor data using a novel machine learning model on a limited number of participants. We also conduct an explainability analysis to interpret the model’s predictions and identify key features associated with depression classification. Methods: In this study, we recruited adults aged 65 years and older through web-based and in-person methods, supported by a McGill University health care facility directory. Participants provided consent, and we collected 6 months of activity and sleep data via nonintrusive Wi-Fi–based sensors, along with Edmonton Frailty Scale and Geriatric Depression Scale data. For depression classification, we proposed a HOPE (Home-Based Older Adults’ Depression Prediction) machine learning model with feature selection, dimensionality reduction, and classification stages, evaluating various model combinations using accuracy, sensitivity, precision, and F1-score. Shapely addictive explanations and local interpretable model-agnostic explanations were used to explain the model’s predictions. Results: A total of 6 participants were enrolled in this study; however, 2 participants withdrew later due to internet connectivity issues. Among the 4 remaining participants, 3 participants were classified as not having depression, while 1 participant was identified as having depression. The most accurate classification model, which combined sequential forward selection for feature selection, principal component analysis for dimensionality reduction, and a decision tree for classification, achieved an accuracy of 87.5%, sensitivity of 90%, and precision of 88.3%, effectively distinguishing individuals with and those without depression. The explainability analysis revealed that the most influential features in depression classification, in order of importance, were “average sleep duration,” “total number of sleep interruptions,” “percentage of nights with sleep interruptions,” “average duration of sleep interruptions,” and “Edmonton Frailty Scale.” Conclusions: The findings from this preliminary study demonstrate the feasibility of using Wi-Fi–based motion sensors for depression classification and highlight the effectiveness of our proposed HOPE machine learning model, even with a small sample size. These results suggest the potential for further research with a larger cohort for more comprehensive validation. Additionally, the nonintrusive data collection method and model architecture proposed in this study offer promising applications in remote health monitoring, particularly for older adults who may face challenges in using wearable devices. Furthermore, the importance of sleep patterns identified in our explainability analysis aligns with findings from previous research, emphasizing the need for more in-depth studies on the role of sleep in mental health, as suggested in the explainable machine learning study. %M 40053734 %R 10.2196/67715 %U https://aging.jmir.org/2025/1/e67715 %U https://doi.org/10.2196/67715 %U http://www.ncbi.nlm.nih.gov/pubmed/40053734 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e66778 %T Exploring Older Adults’ Perspectives and Acceptance of AI-Driven Health Technologies: Qualitative Study %A Wong,Arkers Kwan Ching %A Lee,Jessica Hiu Toon %A Zhao,Yue %A Lu,Qi %A Yang,Shulan %A Hui,Vivian Chi Ching %K artificial intelligence–based health technologies %K health technology %K AI-based health technology %K machine learning %K ML %K artificial intelligence %K AI %K algorithm %K model %K analytics %K perceptions %K acceptability %K gerontology %K geriatrics %K older adult %K elderly %K older person %K older people %K aging %K mobile phone %D 2025 %7 12.2.2025 %9 %J JMIR Aging %G English %X Background: Artificial intelligence (AI) is increasingly being applied in various health care services due to its enhanced efficiency and accuracy. As the population ages, AI-based health technologies could be a potent tool in older adults’ health care to address growing, complex, and challenging health needs. This study aimed to investigate perspectives on and acceptability of the use of AI-led health technologies among older adults and the potential challenges that they face in adopting them. The findings from this inquiry could inform the designing of more acceptable and user-friendly AI-based health technologies. Objective: The objectives of the study were (1) to investigate the attitudes and perceptions of older adults toward the use of AI-based health technologies; (2) to identify potential facilitators, barriers, and challenges influencing older adults’ preferences toward AI-based health technologies; and (3) to inform strategies that can promote and facilitate the use of AI-based health technologies among older adults. Methods: This study adopted a qualitative descriptive design. A total of 27 community-dwelling older adults were recruited from a local community center. Three sessions of semistructured interviews were conducted, each lasting 1 hour. The sessions covered five key areas: (1) general impressions of AI-based health technologies; (2) previous experiences with AI-based health technologies; (3) perceptions and attitudes toward AI-based health technologies; (4) anticipated difficulties in using AI-based health technologies and underlying reasons; and (5) willingness, preferences, and motivations for accepting AI-based health technologies. Thematic analysis was applied for data analysis. The Theoretical Domains Framework and the Capability, Opportunity, Motivation, and Behavior (COM-B) model behavior change wheel were integrated into the analysis. Identified theoretical domains were mapped directly to the COM-B model to determine corresponding strategies for enhancing the acceptability of AI-based health technologies among older adults. Results: The analysis identified 9 of the 14 Theoretical Domains Framework domains—knowledge, skills, social influences, environmental context and resources, beliefs about capabilities, beliefs about consequences, intentions, goals, and emotion. These domains were mapped to 6 components of the COM-B model. While most participants acknowledged the potential benefits of AI-based health technologies, they emphasized the irreplaceable role of human expertise and interaction. Participants expressed concerns about the usability of AI technologies, highlighting the need for user-friendly and tailored AI solutions. Privacy concerns and the importance of robust security measures were also emphasized as critical factors affecting their willingness to adopt AI-based health technologies. Conclusions: Integrating AI as a supportive tool alongside health care providers, rather than regarding it as a replacement, was highlighted as a key strategy for promoting acceptance. Government support and clear guidelines are needed to promote ethical AI implementation in health care. These measures can improve health outcomes in the older adult population by encouraging the adoption of AI-driven health technologies. %R 10.2196/66778 %U https://aging.jmir.org/2025/1/e66778 %U https://doi.org/10.2196/66778 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e57298 %T Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study %A Kim,Taehwan %A Choi,Jung-Yeon %A Ko,Myung Jin %A Kim,Kwang-il %K frailty %K cross-sectional study %K vocal biomarkers %K older adults %K artificial intelligence %K machine learning %K classification model %K self-supervised %D 2025 %7 16.1.2025 %9 %J JMIR Med Inform %G English %X Background: The two most commonly used methods to identify frailty are the frailty phenotype and the frailty index. However, both methods have limitations in clinical application. In addition, methods for measuring frailty have not yet been standardized. Objective: We aimed to develop and validate a classification model for predicting frailty status using vocal biomarkers in community-dwelling older adults, based on voice recordings obtained from the picture description task (PDT). Methods: We recruited 127 participants aged 50 years and older and collected clinical information through a short form of the Comprehensive Geriatric Assessment scale. Voice recordings were collected with a tablet device during the Korean version of the PDT, and we preprocessed audio data to remove background noise before feature extraction. Three artificial intelligence (AI) models were developed for identifying frailty status: SpeechAI (using speech data only), DemoAI (using demographic data only), and DemoSpeechAI (combining both data types). Results: Our models were trained and evaluated on the basis of 5-fold cross-validation for 127 participants and compared. The SpeechAI model, using deep learning–based acoustic features, outperformed in terms of accuracy and area under the receiver operating characteristic curve (AUC), 80.4% (95% CI 76.89%‐83.91%) and 0.89 (95% CI 0.86‐0.92), respectively, while the model using only demographics showed an accuracy of 67.96% (95% CI 67.63%‐68.29%) and an AUC of 0.74 (95% CI 0.73‐0.75). The SpeechAI model outperformed the model using only demographics significantly in AUC (t4=8.705 [2-sided]; P<.001). The DemoSpeechAI model, which combined demographics with deep learning–based acoustic features, showed superior performance (accuracy 85.6%, 95% CI 80.03%‐91.17% and AUC 0.93, 95% CI 0.89‐0.97), but there was no significant difference in AUC between the SpeechAI and DemoSpeechAI models (t4=1.057 [2-sided]; P=.35). Compared with models using traditional acoustic features from the openSMILE toolkit, the SpeechAI model demonstrated superior performance (AUC 0.89) over traditional methods (logistic regression: AUC 0.62; decision tree: AUC 0.57; random forest: AUC 0.66). Conclusions: Our findings demonstrate that vocal biomarkers derived from deep learning–based acoustic features can be effectively used to predict frailty status in community-dwelling older adults. The SpeechAI model showed promising accuracy and AUC, outperforming models based solely on demographic data or traditional acoustic features. Furthermore, while the combined DemoSpeechAI model showed slightly improved performance over the SpeechAI model, the difference was not statistically significant. These results suggest that speech-based AI models offer a noninvasive, scalable method for frailty detection, potentially streamlining assessments in clinical and community settings. %R 10.2196/57298 %U https://medinform.jmir.org/2025/1/e57298 %U https://doi.org/10.2196/57298 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e62930 %T Exploring Dance as a Therapeutic Approach for Parkinson Disease Through the Social Robotics for Active and Healthy Ageing (SI-Robotics): Results From a Technical Feasibility Study %A Bevilacqua,Roberta %A Maranesi,Elvira %A Benadduci,Marco %A Cortellessa,Gabriella %A Umbrico,Alessandro %A Fracasso,Francesca %A Melone,Giovanni %A Margaritini,Arianna %A La Forgia,Angela %A Di Bitonto,Pierpaolo %A Potenza,Ada %A Fiorini,Laura %A La Viola,Carlo %A Cavallo,Filippo %A Leone,Alessandro %A Caroppo,Andrea %A Rescio,Gabriele %A Marzorati,Mauro %A Cesta,Amedeo %A Pelliccioni,Giuseppe %A Riccardi,Giovanni Renato %A Rossi,Lorena %K Parkinson disease %K rehabilitation %K Irish dancing %K balance %K gait %K socially interacting robot %D 2025 %7 14.1.2025 %9 %J JMIR Aging %G English %X Background: Parkinson disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms. Recently, dance has started to be considered an effective intervention for people with PD. Several findings in the literature emphasize the necessity for deeper exploration into the synergistic impacts of dance therapy and exergaming for PD management. Moreover, socially engaging robotic platforms equipped with advanced interaction and perception features offer potential for monitoring patients’ posture and enhancing workout routines with tailored cues. Objective: This paper presents the results of the Social Robotics for Active and Healthy Ageing (SI-Robotics) project, aimed at designing an innovative rehabilitation program targeted at seniors affected by (early-stage) PD. This study therefore aims to assess the usefulness of a dance-based rehabilitation program enriched by artificial intelligence–based exergames and contextual robotic assistance in improving motor function, balance, gait, and quality of life in patients with PD. The acceptability of the system is also investigated. Methods: The study is designed as a technical feasibility pilot to test the SI-Robotics system. For this study, 20 patients with PD were recruited. A total of 16 Irish dance–based rehabilitation sessions of 50 minutes were conducted (2 sessions per week, for 8 wks), involving 2 patients at a time. The designed rehabilitation session involves three main actors: (1) a therapist, (2) a patient, and (3) a socially interacting robot. To stimulate engagement, sessions were organized in the shape of exergames where an avatar shows patients the movements they should perform to correctly carry out a dance-based rehabilitation exercise. Results: Statistical analysis reveals a significant difference on the Performance-Oriented Mobility Assessment scale, both on balance and gait aspects, together with improvements in Short Physical Performance Battery, Unified Parkinson Disease Rating Scale–III, and Timed Up and Go test, underlying the usefulness of the rehabilitation intervention on the motor symptoms of PD. The analysis of the Unified Theory of Acceptance and Use of Technology subscales provided valuable insights into users’ perceptions and interactions with the system. Conclusions: This research underscores the promise of merging dance therapy with interactive exergaming on a robotic platform as an innovative strategy to enhance motor function, balance, gait, and overall quality of life for patients grappling with PD. Trial Registration: ClinicalTrials.gov NCT05005208; https://clinicaltrials.gov/study/NCT05005208 %R 10.2196/62930 %U https://aging.jmir.org/2025/1/e62930 %U https://doi.org/10.2196/62930 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e63494 %T ChatGPT’s Attitude, Knowledge, and Clinical Application in Geriatrics Practice and Education: Exploratory Observational Study %A Cheng,Huai Yong %+ Minneapolis VA Health Care System, 1 Veterans Dr., Minneapolis, MN, 55417, United States, 1 6124672051, wchengwcheng@gmail.com %K ChatGPT %K geriatrics attitude %K ageism %K geriatrics competence %K geriatric syndromes %K polypharmacy %K falls %K aging, older adults %D 2025 %7 3.1.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: The increasing use of ChatGPT in clinical practice and medical education necessitates the evaluation of its reliability, particularly in geriatrics. Objective: This study aimed to evaluate ChatGPT’s trustworthiness in geriatrics through 3 distinct approaches: evaluating ChatGPT’s geriatrics attitude, knowledge, and clinical application with 2 vignettes of geriatric syndromes (polypharmacy and falls). Methods: We used the validated University of California, Los Angeles, geriatrics attitude and knowledge instruments to evaluate ChatGPT’s geriatrics attitude and knowledge and compare its performance with that of medical students, residents, and geriatrics fellows from reported results in the literature. We also evaluated ChatGPT’s application to 2 vignettes of geriatric syndromes (polypharmacy and falls). Results: The mean total score on geriatrics attitude of ChatGPT was significantly lower than that of trainees (medical students, internal medicine residents, and geriatric medicine fellows; 2.7 vs 3.7 on a scale from 1-5; 1=strongly disagree; 5=strongly agree). The mean subscore on positive geriatrics attitude of ChatGPT was higher than that of the trainees (medical students, internal medicine residents, and neurologists; 4.1 vs 3.7 on a scale from 1 to 5 where a higher score means a more positive attitude toward older adults). The mean subscore on negative geriatrics attitude of ChatGPT was lower than that of the trainees and neurologists (1.8 vs 2.8 on a scale from 1 to 5 where a lower subscore means a less negative attitude toward aging). On the University of California, Los Angeles geriatrics knowledge test, ChatGPT outperformed all medical students, internal medicine residents, and geriatric medicine fellows from validated studies (14.7 vs 11.3 with a score range of –18 to +18 where +18 means that all questions were answered correctly). Regarding the polypharmacy vignette, ChatGPT not only demonstrated solid knowledge of potentially inappropriate medications but also accurately identified 7 common potentially inappropriate medications and 5 drug-drug and 3 drug-disease interactions. However, ChatGPT missed 5 drug-disease and 1 drug-drug interaction and produced 2 hallucinations. Regarding the fall vignette, ChatGPT answered 3 of 5 pretests correctly and 2 of 5 pretests partially correctly, identified 6 categories of fall risks, followed fall guidelines correctly, listed 6 key physical examinations, and recommended 6 categories of fall prevention methods. Conclusions: This study suggests that ChatGPT can be a valuable supplemental tool in geriatrics, offering reliable information with less age bias, robust geriatrics knowledge, and comprehensive recommendations for managing 2 common geriatric syndromes (polypharmacy and falls) that are consistent with evidence from guidelines, systematic reviews, and other types of studies. ChatGPT’s potential as an educational and clinical resource could significantly benefit trainees, health care providers, and laypeople. Further research using GPT-4o, larger geriatrics question sets, and more geriatric syndromes is needed to expand and confirm these findings before adopting ChatGPT widely for geriatrics education and practice. %M 39752214 %R 10.2196/63494 %U https://formative.jmir.org/2025/1/e63494 %U https://doi.org/10.2196/63494 %U http://www.ncbi.nlm.nih.gov/pubmed/39752214 %0 Journal Article %@ 2562-7600 %I JMIR Publications %V 7 %N %P e58094 %T Unobtrusive Nighttime Movement Monitoring to Support Nursing Home Continence Care: Algorithm Development and Validation Study %A Strauven,Hannelore %A Wang,Chunzhuo %A Hallez,Hans %A Vanden Abeele,Vero %A Vanrumste,Bart %K nursing home %K agitation %K incontinence %K accelerometer %K unobtrusive %K enuresis %K sensor technology %D 2024 %7 24.12.2024 %9 %J JMIR Nursing %G English %X Background: The rising prevalence of urinary incontinence (UI) among older adults, particularly those living in nursing homes (NHs), underscores the need for innovative continence care solutions. The implementation of an unobtrusive sensor system may support nighttime monitoring of NH residents’ movements and, more specifically, the agitation possibly associated with voiding events. Objective: This study aims to explore the application of an unobtrusive sensor system to monitor nighttime movement, integrated into a care bed with accelerometer sensors connected to a pressure-redistributing care mattress. Methods: A total of 6 participants followed a 7-step protocol. The obtained dataset was segmented into 20-second windows with a 50% overlap. Each window was labeled with 1 of the 4 chosen activity classes: in bed, agitation, turn, and out of bed. A total of 1416 features were selected and analyzed with an XGBoost algorithm. At last, the model was validated using leave one subject out cross-validation (LOSOCV). Results: The trained model attained a trustworthy overall F1-score of 79.56% for all classes and, more specifically, an F1-score of 79.67% for the class “Agitation.” Conclusions: The results from this study provide promising insights in unobtrusive nighttime movement monitoring. The study underscores the potential to enhance the quality of care for NH residents through a machine learning model based on data from accelerometers connected to a viscoelastic care mattress, thereby driving progress in the field of continence care and artificial intelligence–supported health care for older adults. %R 10.2196/58094 %U https://nursing.jmir.org/2024/1/e58094 %U https://doi.org/10.2196/58094 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e54676 %T Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis %A Wang,Tianyi %A Chen,Ruiyuan %A Fan,Ning %A Zang,Lei %A Yuan,Shuo %A Du,Peng %A Wu,Qichao %A Wang,Aobo %A Li,Jian %A Kong,Xiaochuan %A Zhu,Wenyi %+ Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China, 86 51718268, zanglei@ccmu.edu.cn %K lumbar spinal stenosis %K LSS %K machine learning %K ML %K deep learning %K artificial intelligence %K AI %K diagnosis %K spine stenosis %K lumbar %K predictive model %K early detection %K diagnostic %K older adult %K %D 2024 %7 23.12.2024 %9 Review %J J Med Internet Res %G English %X Background: Lumbar spinal stenosis (LSS) is a major cause of pain and disability in older individuals worldwide. Although increasing studies of traditional machine learning (TML) and deep learning (DL) were conducted in the field of diagnosing LSS and gained prominent results, the performance of these models has not been analyzed systematically. Objective: This systematic review and meta-analysis aimed to pool the results and evaluate the heterogeneity of the current studies in using TML or DL models to diagnose LSS, thereby providing more comprehensive information for further clinical application. Methods: This review was performed under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines using articles extracted from PubMed, Embase databases, and Cochrane Library databases. Studies that evaluated DL or TML algorithms assessment value on diagnosing LSS were included, while those with duplicated or unavailable data were excluded. Quality Assessment of Diagnostic Accuracy Studies 2 was used to estimate the risk of bias in each study. The MIDAS module and the METAPROP module of Stata (StataCorp) were used for data synthesis and statistical analyses. Results: A total of 12 studies with 15,044 patients reported the assessment value of TML or DL models for diagnosing LSS. The risk of bias assessment yielded 4 studies with high risk of bias, 3 with unclear risk of bias, and 5 with completely low risk of bias. The pooled sensitivity and specificity were 0.84 (95% CI: 0.82-0.86; I2=99.06%) and 0.87 (95% CI 0.84-0.90; I2=98.7%), respectively. The diagnostic odds ratio was 36 (95% CI 26-49), the positive likelihood ratio (LR+) was 6.6 (95% CI 5.1-8.4), and the negative likelihood ratio (LR–) was 0.18 (95% CI 0.16-0.21). The summary receiver operating characteristic curves, the area under the curve of TML or DL models for diagnosing LSS of 0.92 (95% CI 0.89-0.94), indicating a high diagnostic value. Conclusions: This systematic review and meta-analysis emphasize that despite the generally satisfactory diagnostic performance of artificial intelligence systems in the experimental stage for the diagnosis of LSS, none of them is reliable and practical enough to apply in real clinical practice. Further efforts, including optimization of model balance, widely accepted objective reference standards, multimodal strategy, large dataset for training and testing, external validation, and sufficient and scientific report, should be made to bridge the distance between current TML or DL models and real-life clinical applications in future studies. Trial Registration: PROSPERO CRD42024566535; https://tinyurl.com/msx59x8k %M 39715552 %R 10.2196/54676 %U https://www.jmir.org/2024/1/e54676 %U https://doi.org/10.2196/54676 %U http://www.ncbi.nlm.nih.gov/pubmed/39715552 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e59370 %T Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis %A Battineni,Gopi %A Chintalapudi,Nalini %A Amenta,Francesco %+ Clinical Research, Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University Camerino, Via Madonna Delle Carceri 9, Camerino, 62032, Italy, 39 3331728206, gopi.battineni@unicam.it %K Alzheimer disease %K ML-based diagnosis %K machine learning %K prevalence %K cognitive impairment %K classification %K biomarkers %K imaging modalities %K MRI %K magnetic resonance imaging %K systematic review %K meta-analysis %D 2024 %7 23.12.2024 %9 Review %J JMIR Aging %G English %X Background: To diagnose Alzheimer disease (AD), individuals are classified according to the severity of their cognitive impairment. There are currently no specific causes or conditions for this disease. Objective: The purpose of this systematic review and meta-analysis was to assess AD prevalence across different stages using machine learning (ML) approaches comprehensively. Methods: The selection of papers was conducted in 3 phases, as per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines: identification, screening, and final inclusion. The final analysis included 24 papers that met the criteria. The selection of ML approaches for AD diagnosis was rigorously based on their relevance to the investigation. The prevalence of patients with AD at 2, 3, 4, and 6 stages was illustrated through the use of forest plots. Results: The prevalence rate for both cognitively normal (CN) and AD across 6 studies was 49.28% (95% CI 46.12%-52.45%; P=.32). The prevalence estimate for the 3 stages of cognitive impairment (CN, mild cognitive impairment, and AD) is 29.75% (95% CI 25.11%-34.84%, P<.001). Among 5 studies with 14,839 participants, the analysis of 4 stages (nondemented, moderately demented, mildly demented, and AD) found an overall prevalence of 13.13% (95% CI 3.75%-36.66%; P<.001). In addition, 4 studies involving 3819 participants estimated the prevalence of 6 stages (CN, significant memory concern, early mild cognitive impairment, mild cognitive impairment, late mild cognitive impairment, and AD), yielding a prevalence of 23.75% (95% CI 12.22%-41.12%; P<.001). Conclusions: The significant heterogeneity observed across studies reveals that demographic and setting characteristics are responsible for the impact on AD prevalence estimates. This study shows how ML approaches can be used to describe AD prevalence across different stages, which provides valuable insights for future research. %M 39714089 %R 10.2196/59370 %U https://aging.jmir.org/2024/1/e59370 %U https://doi.org/10.2196/59370 %U http://www.ncbi.nlm.nih.gov/pubmed/39714089 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e63262 %T Moving Toward Meaningful Evaluations of Monitoring in e-Mental Health Based on the Case of a Web-Based Grief Service for Older Mourners: Mixed Methods Study %A Brandl,Lena %A Jansen-Kosterink,Stephanie %A Brodbeck,Jeannette %A Jacinto,Sofia %A Mooser,Bettina %A Heylen,Dirk %K e-mental health %K digital mental health service %K mental health %K digital health %K internet intervention %K monitoring mental health %K monitor %K e-coach %K coaching %K grieve %K mourn %K old %K affective states %K artificial intelligence %K predictive %K repeatedly measured predictors in regression %K fuzzy cognitive map %K algorithm %K AI %D 2024 %7 28.11.2024 %9 %J JMIR Form Res %G English %X Background: Artificial intelligence (AI) tools hold much promise for mental health care by increasing the scalability and accessibility of care. However, current development and evaluation practices of AI tools limit their meaningfulness for health care contexts and therefore also the practical usefulness of such tools for professionals and clients alike. Objective: The aim of this study is to demonstrate the evaluation of an AI monitoring tool that detects the need for more intensive care in a web-based grief intervention for older mourners who have lost their spouse, with the goal of moving toward meaningful evaluation of AI tools in e-mental health. Method: We leveraged the insights from three evaluation approaches: (1) the F1-score evaluated the tool’s capacity to classify user monitoring parameters as either in need of more intensive support or recommendable to continue using the web-based grief intervention as is; (2) we used linear regression to assess the predictive value of users’ monitoring parameters for clinical changes in grief, depression, and loneliness over the course of a 10-week intervention; and (3) we collected qualitative experience data from e-coaches (N=4) who incorporated the monitoring in their weekly email guidance during the 10-week intervention. Results: Based on n=174 binary recommendation decisions, the F1-score of the monitoring tool was 0.91. Due to minimal change in depression and loneliness scores after the 10-week intervention, only 1 linear regression was conducted. The difference score in grief before and after the intervention was included as a dependent variable. Participants’ (N=21) mean score on the self-report monitoring and the estimated slope of individually fitted growth curves and its standard error (ie, participants’ response pattern to the monitoring questions) were used as predictors. Only the mean monitoring score exhibited predictive value for the observed change in grief (R2=1.19, SE 0.33; t16=3.58, P=.002). The e-coaches appreciated the monitoring tool as an opportunity to confirm their initial impression about intervention participants, personalize their email guidance, and detect when participants’ mental health deteriorated during the intervention. Conclusions: The monitoring tool evaluated in this paper identified a need for more intensive support reasonably well in a nonclinical sample of older mourners, had some predictive value for the change in grief symptoms during a 10-week intervention, and was appreciated as an additional source of mental health information by e-coaches who supported mourners during the intervention. Each evaluation approach in this paper came with its own set of limitations, including (1) skewed class distributions in prediction tasks based on real-life health data and (2) choosing meaningful statistical analyses based on clinical trial designs that are not targeted at evaluating AI tools. However, combining multiple evaluation methods facilitates drawing meaningful conclusions about the clinical value of AI monitoring tools for their intended mental health context. %R 10.2196/63262 %U https://formative.jmir.org/2024/1/e63262 %U https://doi.org/10.2196/63262 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e58980 %T Enhancing Frailty Assessments for Transcatheter Aortic Valve Replacement Patients Using Structured and Unstructured Data: Real-World Evidence Study %A Mardini,Mamoun T %A Bai,Chen %A Bavry,Anthony A %A Zaghloul,Ahmed %A Anderson,R David %A Price,Catherine E Crenshaw %A Al-Ani,Mohammad A Z %K transcatheter aortic valve replacement %K frailty %K cardiology %K machine learning %K TAVR %K minimally invasive surgery %K cardiac surgery %K real-world data %K topic modeling %K clinical notes %K electronic health record %K EHR %D 2024 %7 27.11.2024 %9 %J JMIR Aging %G English %X Background: Transcatheter aortic valve replacement (TAVR) is a commonly used treatment for severe aortic stenosis. As degenerative aortic stenosis is primarily a disease afflicting older adults, a frailty assessment is essential to patient selection and optimal periprocedural outcomes. Objective: This study aimed to enhance frailty assessments of TAVR candidates by integrating real-world structured and unstructured data. Methods: This study analyzed data from 14,000 patients between January 2018 and December 2019 to assess frailty in TAVR patients at the University of Florida. Frailty was identified using the Fried criteria, which includes weight loss, exhaustion, walking speed, grip strength, and physical activity. Latent Dirichlet allocation for topic modeling and Extreme Gradient Boosting for frailty prediction were applied to unstructured clinical notes and structured electronic health record (EHR) data. We also used least absolute shrinkage and selection operator regression for feature selection. Model performance was rigorously evaluated using nested cross-validation, ensuring the generalizability of the findings. Results: Model performance was significantly improved by combining unstructured clinical notes with structured EHR data, achieving an area under the receiver operating characteristic curve of 0.82 (SD 0.07), which surpassed the EHR-only model’s area under the receiver operating characteristic curve of 0.64 (SD 0.08). The Shapley Additive Explanations analysis found that congestive heart failure management, back problems, and atrial fibrillation were the top frailty predictors. Additionally, the latent Dirichlet allocation topic modeling identified 7 key topics, highlighting the role of specific medical treatments in predicting frailty. Conclusions: Integrating unstructured clinical notes and structured EHR data led to a notable enhancement in predicting frailty. This method shows great potential for standardizing frailty assessments using real-world data and improving patient selection for TAVR. %R 10.2196/58980 %U https://aging.jmir.org/2024/1/e58980 %U https://doi.org/10.2196/58980 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e53447 %T Using a Device-Free Wi-Fi Sensing System to Assess Daily Activities and Mobility in Low-Income Older Adults: Protocol for a Feasibility Study %A Chung,Jane %A Pretzer-Aboff,Ingrid %A Parsons,Pamela %A Falls,Katherine %A Bulut,Eyuphan %+ Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road NE, Atlanta, GA, 30322, United States, 1 4047277980, jane.chung@emory.edu %K Wi-Fi sensing %K dementia %K mild cognitive impairment %K older adults %K health disparities %K in-home activities %K mobility %K machine learning %D 2024 %7 12.11.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Older adults belonging to racial or ethnic minorities with low socioeconomic status are at an elevated risk of developing dementia, but resources for assessing functional decline and detecting cognitive impairment are limited. Cognitive impairment affects the ability to perform daily activities and mobility behaviors. Traditional assessment methods have drawbacks, so smart home technologies (SmHT) have emerged to offer objective, high-frequency, and remote monitoring. However, these technologies usually rely on motion sensors that cannot identify specific activity types. This group often lacks access to these technologies due to limited resources and technology experience. There is a need to develop new sensing technology that is discreet, affordable, and requires minimal user engagement to characterize and quantify various in-home activities. Furthermore, it is essential to explore the feasibility of developing machine learning (ML) algorithms for SmHT through collaborations between clinical researchers and engineers and involving minority, low-income older adults for novel sensor development. Objective: This study aims to examine the feasibility of developing a novel channel state information–based device-free, low-cost Wi-Fi sensing system, and associated ML algorithms for localizing and recognizing different patterns of in-home activities and mobility in residents of low-income senior housing with and without mild cognitive impairment. Methods: This feasibility study was conducted in collaboration with a wellness care group, which serves the healthy aging needs of low-income housing residents. Prior to this feasibility study, we conducted a pilot study to collect channel state information data from several activity scenarios (eg, sitting, walking, and preparing meals) using the proposed Wi-Fi sensing system continuously over a week in apartments of low-income housing residents. These activities were videotaped to generate ground truth annotations to test the accuracy of the ML algorithms derived from the proposed system. Using qualitative individual interviews, we explored the acceptability of the Wi-Fi sensing system and implementation barriers in the low-income housing setting. We use the same study protocol for the proposed feasibility study. Results: The Wi-Fi sensing system deployment began in November 2022, with participant recruitment starting in July 2023. Preliminary results will be available in the summer of 2025. Preliminary results are focused on the feasibility of developing ML models for Wi-Fi sensing–based activity and mobility assessment, community-based recruitment and data collection, ground truth, and older adults’ Wi-Fi sensing technology acceptance. Conclusions: This feasibility study can make a contribution to SmHT science and ML capabilities for early detection of cognitive decline among socially vulnerable older adults. Currently, sensing devices are not readily available to this population due to cost and information barriers. Our sensing device has the potential to identify individuals at risk for cognitive decline by assessing their level of physical function by tracking their in-home activities and mobility behaviors, at a low cost. International Registered Report Identifier (IRRID): DERR1-10.2196/53447 %M 39531268 %R 10.2196/53447 %U https://www.researchprotocols.org/2024/1/e53447 %U https://doi.org/10.2196/53447 %U http://www.ncbi.nlm.nih.gov/pubmed/39531268 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e54210 %T Developing Independent Living Support for Older Adults Using Internet of Things and AI-Based Systems: Co-Design Study %A Timon,Claire M %A Heffernan,Emma %A Kilcullen,Sophia %A Hopper,Louise %A Lee,Hyowon %A Gallagher,Pamela %A Smeaton,Alan F %A Moran,Kieran %A Hussey,Pamela %A Murphy,Catriona %K independent living %K gerontology %K geriatric %K older adult %K elderly %K aging %K Internet of Things %K IoT %K wearable electronic device %K medical device %K daily living activities %K quality of life %K QoL %K artificial intelligence %K AI %K algorithm %K predictive model %K predictive analytics %K predictive system %K practical model %D 2024 %7 24.10.2024 %9 %J JMIR Aging %G English %X Background: The number of older people with unmet health care and support needs is increasing substantially due to the challenges facing health care systems worldwide. There are potentially great benefits to using the Internet of Things coupled with artificial intelligence to support independent living and the measurement of health risks, thus improving quality of life for the older adult population. Taking a co-design approach has the potential to ensure that these technological solutions are developed to address specific user needs and requirements. Objective: The aim of this study was to investigate stakeholders’ perceptions of independent living and technology solutions, identify stakeholders’ suggestions on how technology could assist older adults to live independently, and explore the acceptability and usefulness of a prototype Internet of Things solution called the NEX system to support independent living for an older adult population. Methods: The development of the NEX system was carried out in 3 key phases with a strong focus on diverse stakeholder involvement. The initial predesign exploratory phase recruited 17 stakeholders, including older adults and family caregivers, using fictitious personas and scenarios to explore initial perceptions of independent living and technology solutions. The subsequent co-design and testing phase expanded this to include a comprehensive web-based survey completed by 380 stakeholders, encompassing older adults, family caregivers, health care professionals, and home care support staff. This phase also included prototype testing at home by 7 older adults to assess technology needs, requirements, and the initial acceptability of the system. Finally, in the postdesign phase, workshops were held between academic and industry partners to analyze data collected from the earlier stages and to discuss recommendations for the future development of the system. Results: The predesign phase revealed 3 broad themes: loneliness and technology, aging and technology, and adopting and using technology. The co-design phase highlighted key areas where technology could assist older adults to live independently: home security, falls and loneliness, remote monitoring by family members, and communication with clients. Prototype testing revealed that the acceptability aspects of the prototype varied across technology types. Ambient sensors and voice-activated assistants were described as the most acceptable technology by participants. Last, the postdesign analysis process highlighted that ambient sensors have the potential for automatic detection of activities of daily living, resulting in key recommendations for future developments and deployments in this area. Conclusions: This study demonstrates the significance of incorporating diverse stakeholder perspectives in developing solutions that support independent living. Additionally, it emphasizes the advantages of prototype testing in home environments, offering crucial insights into the real-world experiences of users interacting with technological solutions. International Registered Report Identifier (IRRID): RR2-10.2196/35277 %R 10.2196/54210 %U https://aging.jmir.org/2024/1/e54210 %U https://doi.org/10.2196/54210 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e59810 %T Determinants of Visual Impairment Among Chinese Middle-Aged and Older Adults: Risk Prediction Model Using Machine Learning Algorithms %A Mao,Lijun %A Yu,Zhen %A Lin,Luotao %A Sharma,Manoj %A Song,Hualing %A Zhao,Hailei %A Xu,Xianglong %K visual impairment %K China %K middle-aged and elderly adults %K machine learning %K prediction model %D 2024 %7 9.10.2024 %9 %J JMIR Aging %G English %X Background: Visual impairment (VI) is a prevalent global health issue, affecting over 2.2 billion people worldwide, with nearly half of the Chinese population aged 60 years and older being affected. Early detection of high-risk VI is essential for preventing irreversible vision loss among Chinese middle-aged and older adults. While machine learning (ML) algorithms exhibit significant predictive advantages, their application in predicting VI risk among the general middle-aged and older adult population in China remains limited. Objective: This study aimed to predict VI and identify its determinants using ML algorithms. Methods: We used 19,047 participants from 4 waves of the China Health and Retirement Longitudinal Study (CHARLS) that were conducted between 2011 and 2018. To envisage the prevalence of VI, we generated a geographical distribution map. Additionally, we constructed a model using indicators of a self-reported questionnaire, a physical examination, and blood biomarkers as predictors. Multiple ML algorithms, including gradient boosting machine, distributed random forest, the generalized linear model, deep learning, and stacked ensemble, were used for prediction. We plotted receiver operating characteristic and calibration curves to assess the predictive performance. Variable importance analysis was used to identify key predictors. Results: Among all participants, 33.9% (6449/19,047) had VI. Qinghai, Chongqing, Anhui, and Sichuan showed the highest VI rates, while Beijing and Xinjiang had the lowest. The generalized linear model, gradient boosting machine, and stacked ensemble achieved acceptable area under curve values of 0.706, 0.710, and 0.715, respectively, with the stacked ensemble performing best. Key predictors included hearing impairment, self-expectation of health status, pain, age, hand grip strength, depression, night sleep duration, high-density lipoprotein cholesterol, and arthritis or rheumatism. Conclusions: Nearly one-third of middle-aged and older adults in China had VI. The prevalence of VI shows regional variations, but there are no distinct east-west or north-south distribution differences. ML algorithms demonstrate accurate predictive capabilities for VI. The combination of prediction models and variable importance analysis provides valuable insights for the early identification and intervention of VI among Chinese middle-aged and older adults. %R 10.2196/59810 %U https://aging.jmir.org/2024/1/e59810 %U https://doi.org/10.2196/59810 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e57926 %T Extracting Critical Information from Unstructured Clinicians’ Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study %A Prakash,Ravi %A Dupre,Matthew E %A Østbye,Truls %A Xu,Hanzhang %+ Department of Family Medicine and Community Health, School of Medicine, Duke Univeristy, 2100 Erwin Rd, Durham, NC, 27710, United States, 1 9196849465, hanzhang.xu@duke.edu %K electronic health record %K EHR %K electric medical record %K EMR %K patient record %K health record %K personal health record %K PHR %K unstructured data %K rule based analysis %K artificial intelligence %K AI %K large language model %K LLM %K natural language processing %K NLP %K deep learning %K Alzheimer's disease and related dementias %K AD %K ADRD %K Alzheimer's disease %K dementia %K geriatric syndromes %D 2024 %7 24.9.2024 %9 Original Paper %J JMIR Aging %G English %X Background: The severity of Alzheimer disease and related dementias (ADRD) is rarely documented in structured data fields in electronic health records (EHRs). Although this information is important for clinical monitoring and decision-making, it is often undocumented or “hidden” in unstructured text fields and not readily available for clinicians to act upon. Objective: We aimed to assess the feasibility and potential bias in using keywords and rule-based matching for obtaining information about the severity of ADRD from EHR data. Methods: We used EHR data from a large academic health care system that included patients with a primary discharge diagnosis of ADRD based on ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes between 2014 and 2019. We first assessed the presence of ADRD severity information and then the severity of ADRD in the EHR. Clinicians’ notes were used to determine the severity of ADRD based on two criteria: (1) scores from the Mini Mental State Examination and Montreal Cognitive Assessment and (2) explicit terms for ADRD severity (eg, “mild dementia” and “advanced Alzheimer disease”). We compiled a list of common ADRD symptoms, cognitive test names, and disease severity terms, refining it iteratively based on previous literature and clinical expertise. Subsequently, we used rule-based matching in Python using standard open-source data analysis libraries to identify the context in which specific words or phrases were mentioned. We estimated the prevalence of documented ADRD severity and assessed the performance of our rule-based algorithm. Results: We included 9115 eligible patients with over 65,000 notes from the providers. Overall, 22.93% (2090/9115) of patients were documented with mild ADRD, 20.87% (1902/9115) were documented with moderate or severe ADRD, and 56.20% (5123/9115) did not have any documentation of the severity of their ADRD. For the task of determining the presence of any ADRD severity information, our algorithm achieved an accuracy of >95%, specificity of >95%, sensitivity of >90%, and an F1-score of >83%. For the specific task of identifying the actual severity of ADRD, the algorithm performed well with an accuracy of >91%, specificity of >80%, sensitivity of >88%, and F1-score of >92%. Comparing patients with mild ADRD to those with more advanced ADRD, the latter group tended to contain older, more likely female, and Black patients, and having received their diagnoses in primary care or in-hospital settings. Relative to patients with undocumented ADRD severity, those with documented ADRD severity had a similar distribution in terms of sex, race, and rural or urban residence. Conclusions: Our study demonstrates the feasibility of using a rule-based matching algorithm to identify ADRD severity from unstructured EHR report data. However, it is essential to acknowledge potential biases arising from differences in documentation practices across various health care systems. %M 39316421 %R 10.2196/57926 %U https://aging.jmir.org/2024/1/e57926 %U https://doi.org/10.2196/57926 %U http://www.ncbi.nlm.nih.gov/pubmed/39316421 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e54655 %T Investigating Acoustic and Psycholinguistic Predictors of Cognitive Impairment in Older Adults: Modeling Study %A Badal,Varsha D %A Reinen,Jenna M %A Twamley,Elizabeth W %A Lee,Ellen E %A Fellows,Robert P %A Bilal,Erhan %A Depp,Colin A %+ IBM Research, 1101 Kitchawan Rd, Yorktown Heights, NY, United States, 1 9149453000, ebilal@us.ibm.com %K acoustic %K psycholinguistic %K speech %K speech marker %K speech markers %K cognitive impairment %K CI %K mild cognitive impairment %K MCI %K cognitive disability %K cognitive restriction %K cognitive limitation %K machine learning %K ML %K artificial intelligence %K AI %K algorithm %K algorithms %K predictive model %K predictive models %K predictive analytics %K predictive system %K practical model %K practical models %K early warning %K early detection %K NLP %K natural language processing %K Alzheimer %K dementia %K neurological decline %K neurocognition %K neurocognitive disorder %D 2024 %7 16.9.2024 %9 Original Paper %J JMIR Aging %G English %X Background: About one-third of older adults aged 65 years and older often have mild cognitive impairment or dementia. Acoustic and psycho-linguistic features derived from conversation may be of great diagnostic value because speech involves verbal memory and cognitive and neuromuscular processes. The relative decline in these processes, however, may not be linear and remains understudied. Objective: This study aims to establish associations between cognitive abilities and various attributes of speech and natural language production. To date, the majority of research has been cross-sectional, relying mostly on data from structured interactions and restricted to textual versus acoustic analyses. Methods: In a sample of 71 older (mean age 83.3, SD 7.0 years) community-dwelling adults who completed qualitative interviews and cognitive testing, we investigated the performance of both acoustic and psycholinguistic features associated with cognitive deficits contemporaneously and at a 1-2 years follow up (mean follow-up time 512.3, SD 84.5 days). Results: Combined acoustic and psycholinguistic features achieved high performance (F1-scores 0.73-0.86) and sensitivity (up to 0.90) in estimating cognitive deficits across multiple domains. Performance remained high when acoustic and psycholinguistic features were used to predict follow-up cognitive performance. The psycholinguistic features that were most successful at classifying high cognitive impairment reflected vocabulary richness, the quantity of speech produced, and the fragmentation of speech, whereas the analogous top-ranked acoustic features reflected breathing and nonverbal vocalizations such as giggles or laughter. Conclusions: These results suggest that both acoustic and psycholinguistic features extracted from qualitative interviews may be reliable markers of cognitive deficits in late life. %M 39283659 %R 10.2196/54655 %U https://aging.jmir.org/2024/1/e54655 %U https://doi.org/10.2196/54655 %U http://www.ncbi.nlm.nih.gov/pubmed/39283659 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e57437 %T Personality and Health-Related Quality of Life of Older Chinese Adults: Cross-Sectional Study and Moderated Mediation Model Analysis %A Dong,Xing-Xuan %A Huang,Yueqing %A Miao,Yi-Fan %A Hu,Hui-Hui %A Pan,Chen-Wei %A Zhang,Tianyang %A Wu,Yibo %K personality %K health-related quality of life %K older adults %K sleep quality %K quality of life %K old %K older %K Chinese %K China %K mechanisms %K psychology %K behavior %K analysis %K hypothesis %K neuroticism %K mediation analysis %K health care providers %K aging %D 2024 %7 12.9.2024 %9 %J JMIR Public Health Surveill %G English %X Background: Personality has an impact on the health-related quality of life (HRQoL) of older adults. However, the relationship and mechanisms of the 2 variables are controversial, and few studies have been conducted on older adults. Objective: The aim of this study was to explore the relationship between personality and HRQoL and the mediating and moderating roles of sleep quality and place of residence in this relationship. Methods: A total of 4123 adults 60 years and older were from the Psychology and Behavior Investigation of Chinese Residents survey. Participants were asked to complete the Big Five Inventory, the Brief version of the Pittsburgh Sleep Quality Index, and EQ-5D-5L. A backpropagation neural network was used to explore the order of factors contributing to HRQoL. Path analysis was performed to evaluate the mediation hypothesis. Results: As of August 31, 2022, we enrolled 4123 older adults 60 years and older. Neuroticism and extraversion were strong influencing factors of HRQoL (normalized importance >50%). The results of the mediation analysis suggested that neuroticism and extraversion may enhance and diminish, respectively, HRQoL (index: β=−.262, P<.001; visual analog scale: β=−.193, P<.001) by increasing and decreasing brief version of the Pittsburgh Sleep Quality Index scores (neuroticism: β=.17, P<.001; extraversion: β=−.069, P<.001). The multigroup analysis suggested a significant moderating effect of the place of residence (EQ-5D-5L index: P<.001; EQ-5D-5L visual analog scale: P<.001). No significant direct effect was observed between extraversion and EQ-5D-5L index in urban older residents (β=.037, P=.73). Conclusions: This study sheds light on the potential mechanisms of personality and HRQoL among older Chinese adults and can help health care providers and relevant departments take reasonable measures to promote healthy aging. %R 10.2196/57437 %U https://publichealth.jmir.org/2024/1/e57437 %U https://doi.org/10.2196/57437 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 11 %N %P e58974 %T Empathic Conversational Agent Platform Designs and Their Evaluation in the Context of Mental Health: Systematic Review %A Sanjeewa,Ruvini %A Iyer,Ravi %A Apputhurai,Pragalathan %A Wickramasinghe,Nilmini %A Meyer,Denny %+ School of Health Sciences, Swinburne University of Technology, PO Box 218, John Street, Hawthorn, 3122, Australia, 61 422587030, rsanjeewa@swin.edu.au %K conversational agents %K chatbots %K virtual assistants %K empathy %K emotionally aware %K mental health %K mental well-being %D 2024 %7 9.9.2024 %9 Review %J JMIR Ment Health %G English %X Background: The demand for mental health (MH) services in the community continues to exceed supply. At the same time, technological developments make the use of artificial intelligence–empowered conversational agents (CAs) a real possibility to help fill this gap. Objective: The objective of this review was to identify existing empathic CA design architectures within the MH care sector and to assess their technical performance in detecting and responding to user emotions in terms of classification accuracy. In addition, the approaches used to evaluate empathic CAs within the MH care sector in terms of their acceptability to users were considered. Finally, this review aimed to identify limitations and future directions for empathic CAs in MH care. Methods: A systematic literature search was conducted across 6 academic databases to identify journal articles and conference proceedings using search terms covering 3 topics: “conversational agents,” “mental health,” and “empathy.” Only studies discussing CA interventions for the MH care domain were eligible for this review, with both textual and vocal characteristics considered as possible data inputs. Quality was assessed using appropriate risk of bias and quality tools. Results: A total of 19 articles met all inclusion criteria. Most (12/19, 63%) of these empathic CA designs in MH care were machine learning (ML) based, with 26% (5/19) hybrid engines and 11% (2/19) rule-based systems. Among the ML-based CAs, 47% (9/19) used neural networks, with transformer-based architectures being well represented (7/19, 37%). The remaining 16% (3/19) of the ML models were unspecified. Technical assessments of these CAs focused on response accuracies and their ability to recognize, predict, and classify user emotions. While single-engine CAs demonstrated good accuracy, the hybrid engines achieved higher accuracy and provided more nuanced responses. Of the 19 studies, human evaluations were conducted in 16 (84%), with only 5 (26%) focusing directly on the CA’s empathic features. All these papers used self-reports for measuring empathy, including single or multiple (scale) ratings or qualitative feedback from in-depth interviews. Only 1 (5%) paper included evaluations by both CA users and experts, adding more value to the process. Conclusions: The integration of CA design and its evaluation is crucial to produce empathic CAs. Future studies should focus on using a clear definition of empathy and standardized scales for empathy measurement, ideally including expert assessment. In addition, the diversity in measures used for technical assessment and evaluation poses a challenge for comparing CA performances, which future research should also address. However, CAs with good technical and empathic performance are already available to users of MH care services, showing promise for new applications, such as helpline services. %M 39250799 %R 10.2196/58974 %U https://mental.jmir.org/2024/1/e58974 %U https://doi.org/10.2196/58974 %U http://www.ncbi.nlm.nih.gov/pubmed/39250799 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e55257 %T Adoption of Artificial Intelligence–Enabled Robots in Long-Term Care Homes by Health Care Providers: Scoping Review %A Wong,Karen Lok Yi %A Hung,Lillian %A Wong,Joey %A Park,Juyoung %A Alfares,Hadil %A Zhao,Yong %A Mousavinejad,Abdolhossein %A Soni,Albin %A Zhao,Hui %+ IDEA Lab, University of British Columbia, Room 259, 2211 Wesbrook Mall, Vancouver, BC, V6T 1Z7, Canada, 1 7782887774, klywong1@mail.ubc.ca %K artificial intelligence %K robot %K long-term care home %K health care provider %K scoping review %K person-centered care %D 2024 %7 27.8.2024 %9 Review %J JMIR Aging %G English %X Background: Long-term care (LTC) homes face the challenges of increasing care needs of residents and a shortage of health care providers. Literature suggests that artificial intelligence (AI)–enabled robots may solve such challenges and support person-centered care. There is a dearth of literature exploring the perspectives of health care providers, which are crucial to implementing AI-enabled robots. Objective: This scoping review aims to explore this scant body of literature to answer two questions: (1) what barriers do health care providers perceive in adopting AI-enabled robots in LTC homes? (2) What strategies can be taken to overcome these barriers to the adoption of AI-enabled robots in LTC homes? Methods: We are a team consisting of 3 researchers, 2 health care providers, 2 research trainees, and 1 older adult partner with diverse disciplines in nursing, social work, engineering, and medicine. Referring to the Joanna Briggs Institute methodology, our team searched databases (CINAHL, MEDLINE, PsycINFO, Web of Science, ProQuest, and Google Scholar) for peer-reviewed and gray literature, screened the literature, and extracted the data. We analyzed the data as a team. We compared our findings with the Person-Centered Practice Framework and Consolidated Framework for Implementation Research to further our understanding of the findings. Results: This review includes 33 articles that met the inclusion criteria. We identified three barriers to AI-enabled robot adoption: (1) perceived technical complexity and limitation; (2) negative impact, doubted usefulness, and ethical concerns; and (3) resource limitations. Strategies to mitigate these barriers were also explored: (1) accommodate the various needs of residents and health care providers, (2) increase the understanding of the benefits of using robots, (3) review and overcome the safety issues, and (4) boost interest in the use of robots and provide training. Conclusions: Previous literature suggested using AI-enabled robots to resolve the challenges of increasing care needs and staff shortages in LTC. Yet, our findings show that health care providers might not use robots because of different considerations. The implication is that the voices of health care providers need to be included in using robots. International Registered Report Identifier (IRRID): RR2-doi:10.1136/bmjopen-2023-075278 %M 39190455 %R 10.2196/55257 %U https://aging.jmir.org/2024/1/e55257 %U https://doi.org/10.2196/55257 %U http://www.ncbi.nlm.nih.gov/pubmed/39190455 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e55126 %T Detection of Mild Cognitive Impairment From Non-Semantic, Acoustic Voice Features: The Framingham Heart Study %A Ding,Huitong %A Lister,Adrian %A Karjadi,Cody %A Au,Rhoda %A Lin,Honghuang %A Bischoff,Brian %A Hwang,Phillip H %+ Department of Epidemiology, Boston University School of Public Health, T3E, 715 Albany Street, Boston, MA, 02118, United States, 1 (617) 358 4049, phhwang@bu.edu %K early detection %K Alzheimer disease and related dementias %K mild cognitive impairment %K digital voice %K machine learning %K smartphone %K mobile phone %D 2024 %7 22.8.2024 %9 Original Paper %J JMIR Aging %G English %X Background: With the aging global population and the rising burden of Alzheimer disease and related dementias (ADRDs), there is a growing focus on identifying mild cognitive impairment (MCI) to enable timely interventions that could potentially slow down the onset of clinical dementia. The production of speech by an individual is a cognitively complex task that engages various cognitive domains. The ease of audio data collection highlights the potential cost-effectiveness and noninvasive nature of using human speech as a tool for cognitive assessment. Objective: This study aimed to construct a machine learning pipeline that incorporates speaker diarization, feature extraction, feature selection, and classification to identify a set of acoustic features derived from voice recordings that exhibit strong MCI detection capability. Methods: The study included 100 MCI cases and 100 cognitively normal controls matched for age, sex, and education from the Framingham Heart Study. Participants' spoken responses on neuropsychological tests were recorded, and the recorded audio was processed to identify segments of each participant's voice from recordings that included voices of both testers and participants. A comprehensive set of 6385 acoustic features was then extracted from these voice segments using OpenSMILE and Praat software. Subsequently, a random forest model was constructed to classify cognitive status using the features that exhibited significant differences between the MCI and cognitively normal groups. The MCI detection performance of various audio lengths was further examined. Results: An optimal subset of 29 features was identified that resulted in an area under the receiver operating characteristic curve of 0.87, with a 95% CI of 0.81-0.94. The most important acoustic feature for MCI classification was the number of filled pauses (importance score=0.09, P=3.10E–08). There was no substantial difference in the performance of the model trained on the acoustic features derived from different lengths of voice recordings. Conclusions: This study showcases the potential of monitoring changes to nonsemantic and acoustic features of speech as a way of early ADRD detection and motivates future opportunities for using human speech as a measure of brain health. %M 39173144 %R 10.2196/55126 %U https://aging.jmir.org/2024/1/e55126 %U https://doi.org/10.2196/55126 %U http://www.ncbi.nlm.nih.gov/pubmed/39173144 %0 Journal Article %@ 2562-7600 %I JMIR Publications %V 7 %N %P e55962 %T AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation %A Lukkien,Dirk R M %A Stolwijk,Nathalie E %A Ipakchian Askari,Sima %A Hofstede,Bob M %A Nap,Henk Herman %A Boon,Wouter P C %A Peine,Alexander %A Moors,Ellen H M %A Minkman,Mirella M N %+ Vilans Centre of Expertise of Long Term Care, Churchilllaan 11, Utrecht, 3505 RE, Netherlands, 31 612416513, d.lukkien@vilans.nl %K decision support systems %K ethics %K long-term care %K responsible innovation %K stakeholder perspectives %D 2024 %7 25.7.2024 %9 Original Paper %J JMIR Nursing %G English %X Background: Although the use of artificial intelligence (AI)–based technologies, such as AI-based decision support systems (AI-DSSs), can help sustain and improve the quality and efficiency of care, their deployment creates ethical and social challenges. In recent years, a growing prevalence of high-level guidelines and frameworks for responsible AI innovation has been observed. However, few studies have specified the responsible embedding of AI-based technologies, such as AI-DSSs, in specific contexts, such as the nursing process in long-term care (LTC) for older adults. Objective: Prerequisites for responsible AI-assisted decision-making in nursing practice were explored from the perspectives of nurses and other professional stakeholders in LTC. Methods: Semistructured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists, and care centralists. A total of 2 imaginary scenarios about AI-DSSs were developed beforehand and used to enable participants articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. In addition, 6 high-level principles for responsible AI were used as probing themes to evoke further consideration of the risks associated with using AI-DSSs in LTC. Furthermore, the participants were asked to brainstorm possible strategies and actions in the design, implementation, and use of AI-DSSs to address or mitigate these risks. A thematic analysis was performed to identify the opportunities and risks of AI-assisted decision-making in nursing practice and the associated prerequisites for responsible innovation in this area. Results: The stance of care professionals on the use of AI-DSSs is not a matter of purely positive or negative expectations but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of the prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to the early identification of care needs, guidance in devising care strategies, shared decision-making, and the workload of and work experience of caregivers. To optimally balance the opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AI-assisted decision-making in nursing practice were identified: (1) regular deliberation on data collection; (2) a balanced proactive nature of AI-DSSs; (3) incremental advancements aligned with trust and experience; (4) customization for all user groups, including clients and caregivers; (5) measures to counteract bias and narrow perspectives; (6) human-centric learning loops; and (7) the routinization of using AI-DSSs. Conclusions: The opportunities of AI-assisted decision-making in nursing practice could turn into drawbacks depending on the specific shaping of the design and deployment of AI-DSSs. Therefore, we recommend considering the responsible use of AI-DSSs as a balancing act. Moreover, considering the interrelatedness of the identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address the different factors important to the responsible embedding of AI-DSSs in practice. %M 39052315 %R 10.2196/55962 %U https://nursing.jmir.org/2024/1/e55962 %U https://doi.org/10.2196/55962 %U http://www.ncbi.nlm.nih.gov/pubmed/39052315 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e54748 %T Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study %A Hu,Xinyue %A Sun,Zenan %A Nian,Yi %A Wang,Yichen %A Dang,Yifang %A Li,Fang %A Feng,Jingna %A Yu,Evan %A Tao,Cui %+ Department of Artificial Intelligence and Informatics, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL, 32224, United States, 1 904 956 3256, tao.cui@mayo.edu %K Alzheimer disease and related dementias %K risk prediction %K graph neural network %K relation importance %K machine learning %D 2024 %7 8.7.2024 %9 Original Paper %J JMIR Aging %G English %X Background: Alzheimer disease and related dementias (ADRD) rank as the sixth leading cause of death in the United States, underlining the importance of accurate ADRD risk prediction. While recent advancements in ADRD risk prediction have primarily relied on imaging analysis, not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes. Objective: The study aims to use graph neural networks (GNNs) with claim data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative, self-explainable method to evaluate relationship importance and its influence on ADRD risk prediction. Methods: We used a variationally regularized encoder-decoder GNN (variational GNN [VGNN]) integrated with our proposed relation importance method for estimating ADRD likelihood. This self-explainable method can provide a feature-important explanation in the context of ADRD risk prediction, leveraging relational information within a graph. Three scenarios with 1-year, 2-year, and 3-year prediction windows were created to assess the model’s efficiency, respectively. Random forest (RF) and light gradient boost machine (LGBM) were used as baselines. By using this method, we further clarify the key relationships for ADRD risk prediction. Results: In scenario 1, the VGNN model showed area under the receiver operating characteristic (AUROC) scores of 0.7272 and 0.7480 for the small subset and the matched cohort data set. It outperforms RF and LGBM by 10.6% and 9.1%, respectively, on average. In scenario 2, it achieved AUROC scores of 0.7125 and 0.7281, surpassing the other models by 10.5% and 8.9%, respectively. Similarly, in scenario 3, AUROC scores of 0.7001 and 0.7187 were obtained, exceeding 10.1% and 8.5% than the baseline models, respectively. These results clearly demonstrate the significant superiority of the graph-based approach over the tree-based models (RF and LGBM) in predicting ADRD. Furthermore, the integration of the VGNN model and our relation importance interpretation could provide valuable insight into paired factors that may contribute to or delay ADRD progression. Conclusions: Using our innovative self-explainable method with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data. %M 38976869 %R 10.2196/54748 %U https://aging.jmir.org/2024/1/e54748 %U https://doi.org/10.2196/54748 %U http://www.ncbi.nlm.nih.gov/pubmed/38976869 %0 Journal Article %@ 2561-7605 %I %V 7 %N %P e53019 %T Assessing the Quality of ChatGPT Responses to Dementia Caregivers’ Questions: Qualitative Analysis %A Aguirre,Alyssa %A Hilsabeck,Robin %A Smith,Tawny %A Xie,Bo %A He,Daqing %A Wang,Zhendong %A Zou,Ning %K Alzheimer’s disease %K information technology %K social media %K neurology %K dementia %K Alzheimer disease %K caregiver %K ChatGPT %D 2024 %7 6.5.2024 %9 %J JMIR Aging %G English %X Background: Artificial intelligence (AI) such as ChatGPT by OpenAI holds great promise to improve the quality of life of patients with dementia and their caregivers by providing high-quality responses to their questions about typical dementia behaviors. So far, however, evidence on the quality of such ChatGPT responses is limited. A few recent publications have investigated the quality of ChatGPT responses in other health conditions. Our study is the first to assess ChatGPT using real-world questions asked by dementia caregivers themselves. Objectives: This pilot study examines the potential of ChatGPT-3.5 to provide high-quality information that may enhance dementia care and patient-caregiver education. Methods: Our interprofessional team used a formal rating scale (scoring range: 0-5; the higher the score, the better the quality) to evaluate ChatGPT responses to real-world questions posed by dementia caregivers. We selected 60 posts by dementia caregivers from Reddit, a popular social media platform. These posts were verified by 3 interdisciplinary dementia clinicians as representing dementia caregivers’ desire for information in the areas of memory loss and confusion, aggression, and driving. Word count for posts in the memory loss and confusion category ranged from 71 to 531 (mean 218; median 188), aggression posts ranged from 58 to 602 words (mean 254; median 200), and driving posts ranged from 93 to 550 words (mean 272; median 276). Results: ChatGPT’s response quality scores ranged from 3 to 5. Of the 60 responses, 26 (43%) received 5 points, 21 (35%) received 4 points, and 13 (22%) received 3 points, suggesting high quality. ChatGPT obtained consistently high scores in synthesizing information to provide follow-up recommendations (n=58, 96%), with the lowest scores in the area of comprehensiveness (n=38, 63%). Conclusions: ChatGPT provided high-quality responses to complex questions posted by dementia caregivers, but it did have limitations. ChatGPT was unable to anticipate future problems that a human professional might recognize and address in a clinical encounter. At other times, ChatGPT recommended a strategy that the caregiver had already explicitly tried. This pilot study indicates the potential of AI to provide high-quality information to enhance dementia care and patient-caregiver education in tandem with information provided by licensed health care professionals. Evaluating the quality of responses is necessary to ensure that caregivers can make informed decisions. ChatGPT has the potential to transform health care practice by shaping how caregivers receive health information. %R 10.2196/53019 %U https://aging.jmir.org/2024/1/e53019 %U https://doi.org/10.2196/53019 %0 Journal Article %@ 2561-7605 %I %V 7 %N %P e52443 %T Positive Emotional Responses to Socially Assistive Robots in People With Dementia: Pilot Study %A Otaka,Eri %A Osawa,Aiko %A Kato,Kenji %A Obayashi,Yota %A Uehara,Shintaro %A Kamiya,Masaki %A Mizuno,Katsuhiro %A Hashide,Shusei %A Kondo,Izumi %K dementia care %K robotics %K emotion %K facial expression %K expression intensity %K long-term care %K sensory modality %K gerontology %K gerontechnology %D 2024 %7 11.4.2024 %9 %J JMIR Aging %G English %X Background: Interventions and care that can evoke positive emotions and reduce apathy or agitation are important for people with dementia. In recent years, socially assistive robots used for better dementia care have been found to be feasible. However, the immediate responses of people with dementia when they are given multiple sensory modalities from socially assistive robots have not yet been sufficiently elucidated. Objective: This study aimed to quantitatively examine the immediate emotional responses of people with dementia to stimuli presented by socially assistive robots using facial expression analysis in order to determine whether they elicited positive emotions. Methods: This pilot study adopted a single-arm interventional design. Socially assistive robots were presented to nursing home residents in a three-step procedure: (1) the robot was placed in front of participants (visual stimulus), (2) the robot was manipulated to produce sound (visual and auditory stimuli), and (3) participants held the robot in their hands (visual, auditory, and tactile stimuli). Expression intensity values for “happy,” “sad,” “angry,” “surprised,” “scared,” and “disgusted” were calculated continuously using facial expression analysis with FaceReader. Additionally, self-reported feelings were assessed using a 5-point Likert scale. In addition to the comparison between the subjective and objective emotional assessments, expression intensity values were compared across the aforementioned 3 stimuli patterns within each session. Finally, the expression intensity value for “happy” was compared between the different types of robots. Results: A total of 29 participants (mean age 88.7, SD 6.2 years; n=27 female; Japanese version of Mini-Mental State Examination mean score 18.2, SD 5.1) were recruited. The expression intensity value for “happy” was the largest in both the subjective and objective assessments and increased significantly when all sensory modalities (visual, auditory, and tactile) were presented (median expression intensity 0.21, IQR 0.09-0.35) compared to the other 2 patterns (visual alone: median expression intensity 0.10, IQR 0.03-0.22; P<.001; visual and auditory: median expression intensity 0.10, IQR 0.04-0.23; P<.001). The comparison of different types of robots revealed a significant increase when all stimuli were presented by doll-type and animal-type robots, but not humanoid-type robots. Conclusions: By quantifying the emotional responses of people with dementia, this study highlighted that socially assistive robots may be more effective in eliciting positive emotions when multiple sensory stimuli, including tactile stimuli, are involved. More studies, including randomized controlled trials, are required to further explore the effectiveness of using socially assistive robots in dementia care. Trial Registration: UMIN Clinical Trials Registry UMIN000046256; https://tinyurl.com/yw37auan %R 10.2196/52443 %U https://aging.jmir.org/2024/1/e52443 %U https://doi.org/10.2196/52443 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e49415 %T Promoting Personalized Reminiscence Among Cognitively Intact Older Adults Through an AI-Driven Interactive Multimodal Photo Album: Development and Usability Study %A Wang,Xin %A Li,Juan %A Liang,Tianyi %A Hasan,Wordh Ul %A Zaman,Kimia Tuz %A Du,Yang %A Xie,Bo %A Tao,Cui %+ Department of Computer Science, North Dakota State University, Quentin Burdick Building Room 258, 1320 Albrecht Boulevard, Fargo, ND, 58105, United States, 1 7012318562, J.Li@ndsu.edu %K aging %K knowledge graph %K machine learning %K reminiscence %K voice assistant %D 2024 %7 23.1.2024 %9 Original Paper %J JMIR Aging %G English %X Background: Reminiscence, a therapy that uses stimulating materials such as old photos and videos to stimulate long-term memory, can improve the emotional well-being and life satisfaction of older adults, including those who are cognitively intact. However, providing personalized reminiscence therapy can be challenging for caregivers and family members. Objective: This study aimed to achieve three objectives: (1) design and develop the GoodTimes app, an interactive multimodal photo album that uses artificial intelligence (AI) to engage users in personalized conversations and storytelling about their pictures, encompassing family, friends, and special moments; (2) examine the app’s functionalities in various scenarios using use-case studies and assess the app’s usability and user experience through the user study; and (3) investigate the app’s potential as a supplementary tool for reminiscence therapy among cognitively intact older adults, aiming to enhance their psychological well-being by facilitating the recollection of past experiences. Methods: We used state-of-the-art AI technologies, including image recognition, natural language processing, knowledge graph, logic, and machine learning, to develop GoodTimes. First, we constructed a comprehensive knowledge graph that models the information required for effective communication, including photos, people, locations, time, and stories related to the photos. Next, we developed a voice assistant that interacts with users by leveraging the knowledge graph and machine learning techniques. Then, we created various use cases to examine the functions of the system in different scenarios. Finally, to evaluate GoodTimes’ usability, we conducted a study with older adults (N=13; age range 58-84, mean 65.8 years). The study period started from January to March 2023. Results: The use-case tests demonstrated the performance of GoodTimes in handling a variety of scenarios, highlighting its versatility and adaptability. For the user study, the feedback from our participants was highly positive, with 92% (12/13) reporting a positive experience conversing with GoodTimes. All participants mentioned that the app invoked pleasant memories and aided in recollecting loved ones, resulting in a sense of happiness for the majority (11/13, 85%). Additionally, a significant majority found GoodTimes to be helpful (11/13, 85%) and user-friendly (12/13, 92%). Most participants (9/13, 69%) expressed a desire to use the app frequently, although some (4/13, 31%) indicated a need for technical support to navigate the system effectively. Conclusions: Our AI-based interactive photo album, GoodTimes, was able to engage users in browsing their photos and conversing about them. Preliminary evidence supports GoodTimes’ usability and benefits cognitively intact older adults. Future work is needed to explore its potential positive effects among older adults with cognitive impairment. %M 38261365 %R 10.2196/49415 %U https://aging.jmir.org/2024/1/e49415 %U https://doi.org/10.2196/49415 %U http://www.ncbi.nlm.nih.gov/pubmed/38261365 %0 Journal Article %@ 2561-7605 %I %V 6 %N %P e46791 %T A Machine Learning–Based Preclinical Osteoporosis Screening Tool (POST): Model Development and Validation Study %A Yang,Qingling %A Cheng,Huilin %A Qin,Jing %A Loke,Alice Yuen %A Ngai,Fei Wan %A Chong,Ka Chun %A Zhang,Dexing %A Gao,Yang %A Wang,Harry Haoxiang %A Liu,Zhaomin %A Hao,Chun %A Xie,Yao Jie %K osteoporosis %K machine learning %K screening tool %K older people %K health care %K Hong Kong %D 2023 %7 7.11.2023 %9 %J JMIR Aging %G English %X Background: Identifying persons with a high risk of developing osteoporosis and preventing the occurrence of the first fracture is a health care priority. Most existing osteoporosis screening tools have high sensitivity but relatively low specificity. Objective: We aimed to develop an easily accessible and high-performance preclinical risk screening tool for osteoporosis using a machine learning–based method among the Hong Kong Chinese population. Methods: Participants aged 45 years or older were enrolled from 6 clinics in the 3 major districts of Hong Kong. The potential risk factors for osteoporosis were collected through a validated, self-administered questionnaire and then filtered using a machine learning–based method. Bone mineral density was measured with dual-energy x-ray absorptiometry at the clinics; osteoporosis was defined as a t score of −2.5 or lower. We constructed machine learning models, including gradient boosting machines, support vector machines, and naive Bayes, as well as the commonly used logistic regression models, for the prediction of osteoporosis. The best-performing model was chosen as the final tool, named the Preclinical Osteoporosis Screening Tool (POST). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) and other metrics. Results: Among the 800 participants enrolled in this study, the prevalence of osteoporosis was 10.6% (n=85). The machine learning–based Boruta algorithm identified 15 significantly important predictors from the 113 potential risk factors. Seven variables were further selected based on their accessibility and convenience for daily self-assessment and health care practice, including age, gender, education level, decreased body height, BMI, number of teeth lost, and the intake of vitamin D supplements, to construct the POST. The AUC of the POST was 0.86 and the sensitivity, specificity, and accuracy were all 0.83. The positive predictive value, negative predictive value, and F1-score were 0.41, 0.98, and 0.56, respectively. Conclusions: The machine learning–based POST was conveniently accessible and exhibited accurate discriminative capabilities for the prediction of osteoporosis; it might be useful to guide population-based preclinical screening of osteoporosis and clinical decision-making. %R 10.2196/46791 %U https://aging.jmir.org/2023/1/e46791 %U https://doi.org/10.2196/46791