AI-Driven Smart Elderly Care: Innovations and Solutions

Topic Information

Dear Colleagues,

This Topic focuses on emerging artificial intelligence (AI) technologies and their transformative potential in the domain of smart elderly care. With the global ageing population projected to double by 2050, there is a pressing need to design and implement intelligent systems that can support independent living, remote monitoring, and predictive health management for older adults. The integration of AI with Internet of Things (IoT), edge computing, robotics, and wearable technologies has enabled the development of smart environments that can assist elderly individuals in maintaining autonomy, improving safety, and enhancing quality of life.

This Topic aims to explore the latest innovations, practical deployments, and theoretical advancements in AI-enabled elderly care systems, including personalised assistive technologies, emotion-aware computing, ethical and privacy-preserving frameworks, and data-driven decision support models.

Topics of interest include, but are not limited to the following:

• AI-driven predictive health analytics and anomaly detection;
• IoT-enabled ambient assisted living (AAL) systems;
• AI and robotics in elderly rehabilitation and surgical assistance;
• Telemedicine and virtual healthcare platforms for ageing populations;
• Privacy-preserving and secure AI models in elderly care environments;
• Generative AI and its application in mental health support for seniors;
• Emotion-aware computing and socially assistive AI companions;
• Wearable sensing technologies integrated with machine learning models;
• Natural language processing (NLP) interfaces for elderly engagement;
• Ethical, regulatory, and policy considerations in AI-based gerontology.

We encourage submissions from multiple disciplinary lenses, including healthcare informatics, human-centred computing, AI ethics, clinical engineering, cybersecurity, and digital health. We welcome original research articles, reviews, technical developments, and application case studies that advance the state of knowledge in this crucial area of intelligent systems for elderly care.

Dr. Mahmoud Elkhodr
Prof. Dr. Ergun Gide
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	6.5	7.3	2020	20.4 Days	CHF 1800	Submit
Geriatrics geriatrics	2.4	3.4	2016	27.7 Days	CHF 1800	Submit
Healthcare healthcare	3.4	5.5	2013	21.5 Days	CHF 2700	Submit
Journal of Ageing and Longevity jal	-	1.8	2021	32.6 Days	CHF 1200	Submit

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Published Papers (6 papers)

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Open AccessReview

From Integrated Care to Learning Systems

by Aristeidis Tsitiridis, Konstantinos Perakis, Athos Antoniades and George Manias

Healthcare 2026, 14(12), 1612; https://doi.org/10.3390/healthcare14121612 - 8 Jun 2026

Viewed by 1068

Abstract

Integrated care is increasingly shaped by digital infrastructures, data governance, and AI-enabled analytics, yet the relevant literature remains fragmented across health-services research, digital health, and machine learning. This article reports a scoping review, conducted in line with PRISMA-ScR guidance, that maps how integrated [...] Read more.

Integrated care is increasingly shaped by digital infrastructures, data governance, and AI-enabled analytics, yet the relevant literature remains fragmented across health-services research, digital health, and machine learning. This article reports a scoping review, conducted in line with PRISMA-ScR guidance, that maps how integrated care models have evolved conceptually, what digital and AI-enabled infrastructures support them, how their clinical, economic, and equity impacts can be evaluated, and what current implementations imply for sustainable scaling. We searched PubMed, Scopus, Semantic Scholar, and Crossref (retrieval date 31 October 2025; forward screening to 31 March 2026) and added grey literature from named policy bodies. The searches identified 15,189 records, reducing to 11,789 after intra- and cross-source deduplication and grey-literature integration; 620 full texts were assessed and 192 were included in the synthesis. Four domains were synthesised: conceptual foundations of integrated care, AI and multimodal analytics, implementation barriers, and digital-governance foundations. We chart the field using a Type I–V maturity scheme (disease, cohort, whole-system, digital-integrated, learning), benchmarked against the Rainbow, MacColl, EMRAM/AMAM, and NHS ICS models. Most deployments cluster at digitally integrated but only weakly adaptive Type IV; recurrent failure modes—temporal blind spots, maintenance debt, semantic drift, and governance gaps—block progression to Type V, and high-profile clinical-AI failures illustrate the cost of attempting Type V analytics on Type IV-or-worse infrastructure. A walk through nine world regions maps each to its current Type I–V position and shows that organisational and payment integration—not digital sophistication alone—is currently the dominant driver of progress. The COMFORTage Integrated Care Model Library is positioned as a workflow of AI agents orchestrating predictive, preventive, and personalised care across the integrated-care lifecycle rather than as a single federated-learning programme. The review positions AI-enabled integrated care less as a finished model than as an emerging design space requiring longitudinal data assets, stewarded model lifecycles, accountable governance, and outcome-based contracting for clinically useful, equitable, and trustworthy learning systems. Full article

(This article belongs to the Topic AI-Driven Smart Elderly Care: Innovations and Solutions)

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13 pages, 807 KB  

Open AccessArticle

Effects of Dual Tasking on Intersegmental Coordination During Walking in People with Parkinson’s Disease: A Cross-Sectional Case–Control Study

by Valéria Feijó Martins, Edilson Fernando de Borba, Lucas de Liz Alves, Leonardo A. Peyré-Tartaruga and Flávia Gomes Martinez

Geriatrics 2026, 11(3), 53; https://doi.org/10.3390/geriatrics11030053 - 28 Apr 2026

Viewed by 1707

Abstract

Background: In dual-task (DT) conditions, individuals must walk while simultaneously engaging in cognitive or motor tasks, which impacts gait performance, especially in older adults and individuals with Parkinson’s disease (PD). Gait impairments in PD under DT conditions have implications for intersegmental coordination. Research [...] Read more.

Background: In dual-task (DT) conditions, individuals must walk while simultaneously engaging in cognitive or motor tasks, which impacts gait performance, especially in older adults and individuals with Parkinson’s disease (PD). Gait impairments in PD under DT conditions have implications for intersegmental coordination. Research question: Intersegmental coordination and gait biomechanics during the DTs were compared between people with PD and older adults. Methods: Thirty-two individuals (16 PD, H&Y 1–3; and 16 older adults) participated in this study and were asked to walk under the following self-selected conditions: single task, DT with a math component, and texting on a cell phone. Spatiotemporal, angular, and intersegmental coordination data were collected using a markerless motion analysis system (OpenCap). Results: Dual-task conditions significantly affected spatiotemporal and kinematic variables, as well as intersegmental coordination. A significant task effect was observed for thigh–shank coordination, whereas no significant group effect was found for the main coordination outcomes. Significance: Significant task effects were observed for intersegmental coordination (thigh–shank CRP), with no significant group differences. The concurrent demands of processing visual and motor information for texting and walking lead to significant reductions in gait speed and lower limb movement, as well as altered intersegmental coordination, with task demands rather than disease status being the primary driver of coordination changes. Full article

(This article belongs to the Topic AI-Driven Smart Elderly Care: Innovations and Solutions)

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17 pages, 240 KB  

Open AccessArticle

Beyond Access: Telehealth Readiness, Trust, and Early Use Among Jordanian Patients with Chronic Illness

by Ahmad Rajeh Saifan, Murad Sawalha, Ibtisam A. Alarabyat, Hanan F. Alharbi, Zyad Saleh, Osama Alkouri, Rani Shatnawi, Dana Anwer Abujaber, Rami Eid Samarah and Nabeel Al-Yateem

Healthcare 2026, 14(9), 1118; https://doi.org/10.3390/healthcare14091118 - 22 Apr 2026

Viewed by 833

Abstract

Background: Telehealth has expanded access to care for people with chronic diseases, but little is known about how patients in Jordan become activated, motivated, and ready to use these services, particularly during early adoption. Aim: To explore how patients with chronic diseases [...] Read more.

Background: Telehealth has expanded access to care for people with chronic diseases, but little is known about how patients in Jordan become activated, motivated, and ready to use these services, particularly during early adoption. Aim: To explore how patients with chronic diseases in Jordan describe their initial activation, readiness, and experiences with telehealth services. Methods: This exploratory qualitative study used interviews with 14 purposively selected adults with chronic diseases from three hospitals in Jordan. Data was analyzed using Braun and Clarke’s six-step thematic analysis. Results: Four interrelated themes emerged. First, patients valued telehealth for preserving independence and ensuring continuity of care, particularly by reducing reliance on family members for transportation to health facilities. Second, readiness was shaped by geography, mobility, and finances. Although telehealth reduced transport costs and lost wages, patients still had to pay for devices and internet access, creating an economic paradox for poorer patients. Third, participation was supported by families but hindered by low digital literacy, platform changes, and unstable internet connectivity. Fourth, trust in telehealth was conditional and depended on patients’ perceptions of convenience and responsiveness. Conclusions: Readiness to use telehealth was relational, structural, experiential, and conditional rather than purely individual. Patients with chronic diseases in Jordan need hybrid care models that engage families and leverage affordable digital technologies to support sustained telehealth use for disease management. Full article

(This article belongs to the Topic AI-Driven Smart Elderly Care: Innovations and Solutions)

13 pages, 475 KB  

Open AccessArticle

The Effect of Internet Use on Older Adults’ Executive Function: The Chain Mediation Effect of Social Participation and Loneliness

by Jing Xu, Na Li, Yu Jian, Xin Yang and Xianwen Li

Healthcare 2026, 14(8), 1071; https://doi.org/10.3390/healthcare14081071 - 17 Apr 2026

Viewed by 586

Abstract

Background/Objectives: This study aimed to explore the association between internet use and executive function among older adults and the mediating role of social participation and loneliness in internet use and executive function. Methods: A cross-sectional study was conducted among 439 community-dwelling [...] Read more.

Background/Objectives: This study aimed to explore the association between internet use and executive function among older adults and the mediating role of social participation and loneliness in internet use and executive function. Methods: A cross-sectional study was conducted among 439 community-dwelling older adults (≥60 years) in Nanjing, China, from September to December 2022. Participants were selected using simple random sampling and assessed with four standardized instruments: the Internet Use Questionnaire, the Social Participation Capacity Assessment, the six-item UCLA Loneliness Scale (ULS-6), and the Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A). Data were analyzed with the SPSS 21.0 software for descriptive statistics and correlation analysis and the AMOS 23.0 software for structural equation modeling to test the chain mediation effects. Model fit was evaluated using Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), and Weighted Root Mean Square (WRMR), with bootstrap resampling for indirect effect estimation. Results: The results showed that internet use was positively correlated with loneliness (r = 0.203, p < 0.01), social participation impairment (r = 0.193, p < 0.01), and executive function (r = 0.420, p < 0.01). Structural equation modeling showed that greater internet use was significantly associated with poorer executive function (β = 0.306, p < 0.01). These associations were partially explained by pathways involving social participation and loneliness through three indirect pathways: internet use via social participation (indirect effect = 0.087, 18.3% of the total effect); internet use via loneliness (indirect effect = 0.049, 10.3%); and internet use via social participation and then loneliness in sequence (indirect effect = 0.035, 7.1%). Conclusions: In community-dwelling older adults, more frequent internet use was associated with greater executive function impairment through mechanisms involving reduced social participation and increased loneliness. Therefore, there is a need to limit excessive internet use while promoting social participation and reducing isolation, which can have the greatest benefits for executive functioning in older adults. Full article

(This article belongs to the Topic AI-Driven Smart Elderly Care: Innovations and Solutions)

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13 pages, 237 KB  

Open AccessArticle

An Artificial Intelligence-Assisted Smartphone Application for Improving Dietary Quality Among Frail Older Adults: A Quasi-Experimental Study

by Kayo Kurotani, Hikaru Tanabe, Keiji Yanai, Kazunori Sakamoto and Kazunori Ohkawara

Geriatrics 2025, 10(6), 160; https://doi.org/10.3390/geriatrics10060160 - 4 Dec 2025

Cited by 1 | Viewed by 2401

Abstract

Background/Objectives: Although information and communication technology (ICT) offers opportunities to address challenges, evidence among frail populations is limited. We aimed to evaluate the effectiveness and feasibility of an ICT-based intervention incorporating an artificial intelligence (AI)-assisted smartphone dietary application and group communication tools [...] Read more.

Background/Objectives: Although information and communication technology (ICT) offers opportunities to address challenges, evidence among frail populations is limited. We aimed to evaluate the effectiveness and feasibility of an ICT-based intervention incorporating an artificial intelligence (AI)-assisted smartphone dietary application and group communication tools to improve dietary quality and social connection among community-dwelling older adults with frailty. Methods: A non-randomized, quasi-experimental study was conducted among 29 older adults (≥65 years) in Tokyo, Japan. Participants were assigned to the intervention (n = 11) or control (n = 18) group. The 3-month intervention included weekly photo uploads of meals via an AI-based dietary application providing automated image analysis and personalized feedback, supervised by registered dietitians, along with peer communication through a group chat. The primary outcome was dietary quality. The secondary outcomes included body weight, body mass index (BMI), skin carotenoid score, and loneliness. Results: The adjusted Japanese Food Guide Spinning Top Score at 3-month follow-up was 49.0 (standard error [SE] = 2.6) and 39.5 (SE = 2.0) in the intervention and control groups, respectively. The adjusted mean difference between groups was +9.5 (95% confidence interval: 2.3 to 16.7, p = 0.01). After using analysis of covariance for adjusting for respective baseline values, age, education status, and antihypertension drug use, no statistically significant between-group differences were observed at 3-month follow-up for any secondary outcomes. Conclusions: AI-based dietary intervention and peer communication effectively improved dietary quality among older adults, highlighting the potential of such an intervention to promote healthier eating habits in this population. Full article

(This article belongs to the Topic AI-Driven Smart Elderly Care: Innovations and Solutions)

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17 pages, 1888 KB  

Open AccessSystematic Review

Comparing the Effects of AI-Assisted and Traditional Exercise on Physical Health Outcomes in Older Adults: A Systematic Review and Meta-Analysis

by Sijing Fan, Xin Tan, Hongyun Zheng, Yicong Cui, Xiaotong Du, Boqiao Huang, Jingzhan Ren, Xinming Ye and Wen Fang

Healthcare 2025, 13(23), 2999; https://doi.org/10.3390/healthcare13232999 - 21 Nov 2025

Cited by 3 | Viewed by 2818 | Correction

Abstract

Objective: Exercise is widely recognized as an effective non-pharmacological intervention to maintain health in older adults. With advances in artificial intelligence (AI), AI-assisted exercise has emerged as a novel rehabilitation approach, yet its comparative effectiveness against traditional and software-assisted programs remains unclear. This [...] Read more.

Objective: Exercise is widely recognized as an effective non-pharmacological intervention to maintain health in older adults. With advances in artificial intelligence (AI), AI-assisted exercise has emerged as a novel rehabilitation approach, yet its comparative effectiveness against traditional and software-assisted programs remains unclear. This study aimed to evaluate and rank the relative effectiveness of these interventions on multiple physical and psychological outcomes using a network meta-analysis (NMA). Methods: Following the PRISMA-NMA guidelines, we systematically searched PubMed, Embase, Cochrane Library, Web of Science, and Scopus up to June 2025. Eligible studies were randomized controlled trials (RCTs) involving adults ≥ 60 years comparing AI-assisted, software-assisted, and conventional upper/lower limb rehabilitation. Six outcomes were analyzed: gait, balance, range of motion (ROM), muscle strength, cognitive function, and quality of life (QOL). Stata 17.0 was used to conduct the NMA, calculating the standardized mean differences (SMDs) and SUCRA rankings, with assessments of heterogeneity and risk of bias. Results: Seventy RCTs with 808 participants were included. All active interventions outperformed the placebo. AI-assisted programs showed the strongest effects on gait (SMD = 1.33) and balance (SMD = 0.76), while software-assisted interventions ranked highest for ROM (SMD = 0.69) and QOL (SMD = 1.06). Both AI and software interventions improved cognition and muscle strength. Heterogeneity was low (I² ≤ 38.5%). Subgroup analysis indicated that AI-based methods were superior to traditional rehabilitation, although differences among novel interventions were not statistically significant. Conclusions: AI-assisted exercise is highly effective for gait and balance, while software-assisted approaches excel in ROM and QOL. These interventions hold promise for community and home-based rehabilitation. Future studies should investigate integrated “AI + traditional” models and incorporate biomechanical and neurophysiological indicators to optimize personalized care. Full article

(This article belongs to the Topic AI-Driven Smart Elderly Care: Innovations and Solutions)

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