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Biological age (BA) is increasingly recognized as a valuable alternative to chronological age (CA) for assessing an individual's health and aging status. However, existing models are based on limited clinical parameters and have not thoroughly integrated morbidity and mortality data. This study aimed to develop and validate a novel transformer-based model, referred to as the BA - CA gap model, for BA estimation that incorporates morbidity and mortality information to improve predictive accuracy and enhance clinical use in the early identification of the risk of age-related diseases. We retrospectively analyzed data from 151,281 adults aged 18 years or older who underwent routine health checkups between 2003 and 2020. Participants were classified into normal, predisease, and disease groups based on comorbidities (diabetes mellitus, hypertension, and dyslipidemia) to evaluate the model's ability to discriminate health status along a clinically relevant spectrum. Variables with less than 50% missingness had missing values imputed using the mean, while features with 50% or more missingness were excluded. We develop a custom transformer architecture that learns multiple objectives simultaneously, including input feature reconstruction, BA and CA alignment, health status discrimination, and mortality prediction. Model training used unsupervised and self-supervised strategies. We compared our model's performance with conventional BA estimation approaches, including Klemera and Doubal's method, a CA cluster-based model, and a deep neural network, by examining BA gap distributions, health status stratification, and mortality prediction. The proposed BA - CA gap model provided a more accurate reflection of health status and superior stratification of mortality risk than existing methods. The model effectively distinguished among normal, predisease, and disease groups, with a clear gradient of BA gap values. Kaplan-Meier analyses demonstrated stronger discrimination of future mortality in men, while a similar but not statistically significant trend was observed in women. Sensitivity analyses across multiple random splits and training subsets confirmed the robustness of the model's performance. By integrating morbidity and mortality information within a transformer-based framework, the BA - CA gap model offers a more granular and clinically meaningful assessment of aging and health status than CA alone. This approach supports the potential for personalized health management and risk stratification, although external validation in diverse populations is warranted to further confirm its generalizability.

As the population ages, demand for health services among older adults and caregivers increases. Digital health services help meet this demand. However, acceptance and experiences differ between older adults and caregivers. This study aims to explore the specific experiences of home-dwelling older adults and their caregivers with digital health services, identify their willingness to use these services and the factors influencing their adoption, and identify their service needs to inform the subsequent design and optimization of digital health products and services. From December 2023 to February 2024, a descriptive qualitative study was conducted. Researchers used purposive and maximum variation sampling to recruit 18 home-dwelling older adults and 17 family caregivers, including 10 matched dyads. Participants came from 7 community health service centers and a tertiary hospital in Hefei, China. Open-ended, semistructured, face-to-face interviews were conducted separately. Data were analyzed using conventional content analysis. Four main themes emerged. The first were the application characteristics, including usage situations and preferred functions. The second theme described specific service experiences, both positive and negative. The third was the influencing factors. Promoting factors included health needs, user experience, and subjective norms. Obstructive factors differed between groups and included low digital literacy, weak economic foundation, service security concerns, and lack of time. The fourth theme covered suggestions and expectations for improvement. Older adults appreciated the advantages but often had suboptimal experiences due to complex designs and threats to personal dignity. Conversely, caregivers valued efficiency but reported low use, hindered by time constraints and concerns about privacy and safety. This study reveals a critical gap between the potential and actual use of digital health services. Older adults face barriers with usability and digital literacy. Caregivers struggle to integrate these services and have trust issues. Future optimization requires action at several levels. Government support is needed through policies, authoritative platforms, and subsidies. Service providers should offer age-friendly, personalized design, and continuous support. Social support systems are also essential, such as digital reciprocity from family and peer help. These strategies are critical for enhancing user experience, bridging the digital divide, and enabling both older adults and their caregivers to fully benefit from digital health technologies.

Limited research has investigated the influence of patient engagement on the long-term effects of mobile health (mHealth) interventions, particularly among older adults. This study aimed to examine the long-term impact of a social media-driven mHealth intervention on blood pressure control among older Chinese individuals with hypertension, through repeated measurements of patient engagement and outcomes at 5 preset time points. The study included older Chinese individuals with hypertension between 2017 and 2022. Participants received a hypertension self-management program via the WeChat social media app (Tencent Holdings Ltd), which provided clinically based digital coaching. Blood pressure measurements were taken repeatedly using a home blood pressure monitor (HBPM) connected to the app at baseline, 3, 6, 9, and 12 months. Patient engagement was evaluated based on the frequency of completed measurements at corresponding follow-ups. Latent growth curve models (LGCMs) served to assess the impact of patient engagement on blood pressure among older individuals with hypertension across preset points. A total of 1723 patients completed the 12-month follow-up (average age 70.1, SD 6.8 years; 890/1723, 51.7% female; and baseline systolic blood pressure 137.2 mm Hg). LGCMs revealed systolic blood pressure decreased significantly over 1 year, notably at 9 months (131 mm Hg, β9=3.244, P<.001), and continued up to 12 months (131.6mm Hg, β12=2.827, P<.001). In addition, a higher frequency of completed measurements was associated with better systolic blood pressure control at 3, 6, 9, and 12 months (β3=-0.016, P=.002; β6=-0.006, P=.02; β9=-0.002, P=.44; β12=-0.003, P=.02). These results remained significant even after accounting for age, sex, and comorbidity status. This study, using LGCMs and repeated measures data, revealed a significant positive impact of patient engagement on long-term blood pressure control in mHealth interventions targeting older individuals with hypertension. These findings stress the importance of integration of patient-centered engagement approach into mHealth programs designed for chronic disease management in aging populations.

Although eHealth has shown promise in managing chronic diseases, there remains a substantial digital divide among older adults. The concept of a growth mindset, based on psychological theory, offers a new direction and potential breakthrough for addressing this dilemma. This study aims to develop and explore the feasibility and efficacy of a growth mindset intervention for older adults with chronic diseases and their positive response to eHealth. A randomized controlled trial was conducted at the internal medicine departments of a hospital in Hangzhou, Zhejiang Province, China, from September 2021 to October 2022. A total of 77 older patients with chronic disease initially participated in the study. The mean age of the participants was 67.16 (SD 7.04) years, with 42.86% (33/77) being women and 57.14% (44/77) being men. The experimental group received an eHealth program intervention plus a growth mindset intervention over 12 weeks, with weekly sessions for the first 6 weeks and biweekly follow-up phone calls for the next 6 weeks. Each session lasted at least 25-45 minutes. Data were collected using a personal information form, the Implicit Theories of Intelligence Scale-6 (ITIS-6), and a questionnaire on knowledge, willingness, confidence, and practice of smart medicine (KWCP-SM). Measurements were taken at the beginning of the study (T0), immediately after the 6 weeks of training provided to the experimental group (T1), and after the 12 weeks of training for the intervention (T2). Data were analyzed using repeated-measures analysis of variance and analysis of covariance. The final sample comprised 74 participants, of which 36 were in the experimental group and 38 in the control group. After 12 weeks of intervention, the level of growth mindset was significantly higher in the intervention group (P<.05) and significant group × time interaction was observed (Wald=11.57; P<.05) between the two groups. KWCP-SM scores increased in both groups (P<.05), with more significant changes in the intervention group. This study demonstrated the effectiveness of the intervention program in improving the growth mindset level of older adults with chronic diseases and bridging the \"digital divide\" among them. Future studies should refine this intervention, considering the characteristics and needs of this population, to create fault-tolerant and lifelong growth environments that enhance growth mindset in older adults.

As rapidly aging populations become a worldwide phenomenon, early detection and prompt management of chronic disease become essential to support healthy aging. Community-based health screenings, a key component of this strategy, often struggle with poor follow-up rates, limiting their long-term impact. Given the untapped potential of youth volunteers and the urgent need for a scalable approach to improve continuity of care post health screenings, we developed HealthStart: a structured, theory-based program that empowers these older adults to take greater ownership of their health and their chronic conditions with the support of youth community health volunteers (CHVs). This study aimed to describe the development, implementation, and early outcomes of HealthStart-an intergenerational, layperson-led health coaching program-and summarize operational lessons to guide similar models in Asian communities. HealthStart adopted an intergenerational service-learning approach modeled on a self-determination theory-based layperson-led health coaching framework. Each HealthStart team consisted of 1 health care volunteer (HCV) and 4 youth CHVs. All volunteers underwent blended training and were assessed for layperson-led health coaching readiness. Between September 2022 and June 2023 in Singapore, youth CHVs empowered adult participants aged 40 years and older after their health screening to (1) learn about their chronic diseases, (2) learn at least one digital health app, (3) enroll with a primary care provider, and (4) set a lifestyle goal (based on the Specific, Measurable, Achievable, Realistic/Relevant, and Time-bound [SMART] framework for goal setting) and achieve it. We used an implementation-focused case study design using descriptive statistics and volunteer-participant feedback to evaluate feasibility and outcomes. Of 236 eligible individuals, 192 enrolled. Participants had a mean age of 67 (SD 9.6) years; 52.1% (n=100) of participants were female, with a majority of Chinese ethnicity, having completed primary or secondary school education, residing in self-owned flats, and living in 3-room public housing. Follow-up rate with primary care increased from 42.7% (82/148) preprogram to 84.5% (125/148) postprogram (χ21=43; P<.001). In total, 58 HCVs were recruited, comprising 26 nurses and 6 doctors, with the remainder as allied health professionals. A total of 33 were trained and deployed. The mean age of HCVs was 37 years old, and 24 (72.7%) were female. Furthermore, 149 youth CHVs were recruited, 138 trained, and 102 deployed. The mean age of the youth CHVs who were deployed was 24 years, and 75 (73.5%) were female. Reflections included the importance of volunteer competency and selection criteria, tiering of participant intervention, tapping on community assets, adoption of a social prescription framework, importance of alignment with population health policies, and cultivating intergenerational relationships. HealthStart demonstrates the feasibility and acceptability of a structured, intergenerational, layperson-led health coaching model embedded in primary care. We identify key lessons learned in the conceptualization and implementation of the program that may inform the design of similar volunteer-enabled initiatives for harnessing laypersons, an often-underused asset, to promote health in the community.

Digital health literacy (DHL) has the potential to improve health among older adults by enhancing access to health-related information and health care services. The aim of this study was to analyze the relationship between DHL and technology commitment in adults aged 65 years and older, while also investigating possible gender differences. The analytical sample consisted of 1824 individuals. The analysis included descriptive comparisons in terms of DHL, technology acceptance, competency, support, and internet use. Multivariate regression models (generalized linear models) were applied in order to test the association between DHL and technology commitment, controlling for internet use as well as health-related and sociodemographic characteristics. Male and female participants did not differ in terms of DHL (mean score: 3.5, SD 1.2 [men] and 3.5, SD 1.3 [women]; P=.70); however, male participants reported significantly higher technology acceptance (P<.001) and higher technology competencies (P<.001), but less support with regard to technology use (P<.001). Within regression models, only higher technology acceptance (coefficient=0.023, 95% CI 0.006-0.041; P=.01) and support (coefficient=0.027, 95% CI 0.014-0.040; P<.001) were significantly linked to greater DHL. The subgroup analysis revealed that DHL was significantly associated with technology acceptance among men (coefficient=0.036, 95% CI 0.012-0.060; P=.003) but not women (coefficient=0.024, 95% CI 0.008-0.040; P=.44). According to the current results, DHL is highly related to technology commitment. Gender differences should be taken into account when developing and evaluating appropriate interventions to improve DHL by addressing the acceptance of technologies and optimizing support infrastructures.

Infectious diseases are among the leading causes of death and disability and are recognized as a major cause of health loss globally. At the same time, frailty as a geriatric syndrome is a rapidly growing major public health problem. However, few studies have investigated the incidence and risk of infectious diseases in frail older people. Thus, research on frailty and infectious diseases is urgently needed. The purpose of this study was to evaluate the association between frailty and infectious diseases among older adults aged 65 years and older. In this prospective observational cohort study, we have analyzed the infectious disease prevalence outcomes of older adults aged 65 years and older who participated in frailty epidemiological surveys from March 1, 2018, to March 2023 in Dalang Town, Dongguan City, and from March 1, 2020, to March 2023 in Guancheng Street, Dongguan City. This study has an annual on-site follow-up. Incidence data for infectious diseases were collected through the Chinese Disease Control and Prevention Information System-Infectious Disease Monitoring and Public Health Emergency Monitoring System. A project-developed frailty assessment scale was used to assess the frailty status of study participants. We compared the incidence rate ratios (IRR) of each disease across frailty status, age, and gender to determine the associations among frailty, gender, age, and infectious diseases. Cox proportional hazards regression was conducted to identify the effect of frailty on the risk of demographic factors and frailty on the risk of infectious diseases, with estimations of the hazard ratio and 95% CI. A total of 235 cases of 12 infectious diseases were reported during the study period, with an incidence of 906.21/100,000 person-years in the frailty group. In the same age group, the risk of infection was higher in men than women. Frail older adults had a hazard ratio for infectious diseases of 1.50 (95% CI 1.14-1.97) compared with healthy older adults. We obtained the same result after sensitivity analyses. For respiratory tract-transmitted diseases (IRR 1.97, 95% CI 1.44-2.71) and gastrointestinal tract-transmitted diseases (IRR 3.67, 95% CI 1.39-10.74), frail older adults are at risk. Whereas no significant association was found for blood-borne, sexually transmitted, and contact-transmitted diseases (IRR 0.76, 95% CI 0.37-1.45). Our study provides additional evidence that frailty components are significantly associated with infectious diseases. Health care professionals must pay more attention to frailty in infectious disease prevention and control.

Physical frailty and cognitive frailty are increasingly recognized as critical geriatric syndromes among older adults with diabetes, contributing to adverse outcomes such as disability, hospitalization, and mortality. Early identification of individuals at high risk is therefore essential for timely prevention and intervention. Although a growing number of prediction models have been developed for this population, evidence regarding their methodological rigor, predictive performance, and generalizability remains fragmented. This study aims to evaluate and characterize existing models for detecting or predicting physical frailty and cognitive frailty in older adults with diabetes. PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP databases were searched from their inception to December 2025. Retrospective, cross-sectional, and prospective studies that developed or validated models predicting frailty or cognitive frailty in older adults with diabetes were included. The Prediction Model Study Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability. Random effects meta-analyses using the Hartung-Knapp-Sidik-Jonkman method were conducted to synthesize model performance, including the pooled area under the receiver operating characteristic curve (AUC). Heterogeneity was explored through subgroup and sensitivity analyses. Small study effects were evaluated using funnel plots, the Egger test, and the Deeks funnel plot asymmetry test. A total of 24 studies comprising 32 diagnostic models were included. The overall pooled analysis demonstrated an AUC of 0.851 (95% CI 0.820-0.882) with a 95% prediction interval of 0.710-0.992, sensitivity of 0.810 (95% CI 0.740-0.850), and specificity of 0.850 (95% CI 0.810-0.890). Statistical comparisons in the modeling approach revealed that logistic regression models achieved a significantly higher pooled AUC (0.850) compared with machine learning models (0.785; P=.003). Similarly, retrospective studies demonstrated superior performance, with an AUC of 0.900 compared with 0.843 for cross-sectional studies (P=.03). Conversely, no significant differences were observed across subgroups stratified by data source (P=.42), patient characteristics (P=.77), validation methods (P=.16), or specific outcomes (P=.94). The most common predictors identified were depression, age, and regular exercise; however, all included studies were assessed as having a high risk of bias. To our knowledge, this review provides the first comprehensive synthesis of models for risk stratification of physical frailty and cognitive frailty in older adults with diabetes. The findings indicate that existing models demonstrate satisfactory discrimination; specifically, CIs confirmed a robust average effect, while prediction intervals suggested that performance in future settings, though variable, is likely to remain acceptable. However, clinical utility is currently constrained by high risk of bias and limited external validation. Future research must prioritize rigorous, prospective, multicenter studies adhering to standard reporting guidelines (eg, TRIPOD [Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis]) to establish valid, generalizable, and clinically actionable prognostic instruments.

Health care systems are increasingly facing challenges posed by the aging of populations. In particular, hospitalization, both initial and subsequent, is often observed among older adult patients. However, research suggests that nearly 23% of all hospitalizations could be avoided. In this perspective, remote patient monitoring (RPM) systems are emerging as a promising solution, enabling professionals to detect and manage patient complexities early within home-based care settings. This study aims to provide additional analyses regarding the impact of the EPOCA RPM system for polypathological older adult patients on the total number of unplanned hospitalization days and admissions, as well as emergency department (ED) visits. In a prior study, we evaluated the impact when the operator of the RPM system is a geriatrician. In this study, we assess the impact when the general practitioner is the operator. We used a retrospective, before-and-after cohort design. Polypathological older adult patients aged 70 and older, who benefited from the EPOCA RPM system for at least 1 year (between February 2022 and August 2024), were included in the analysis. We compared the outcomes between the previous year (Y-1) and the follow-up year (Y) by the EPOCA RPM system. Statistical analyses were significant at P value <.05. In total, 80 patients were included in the analysis, with an average age of 87. The results showed a significant reduction (P<.001) between Y-1 and Y in the total number of unplanned hospital admissions (by 57%), hospitalization days (by 49%), and ED visits (by 62%). Our findings reflected a significant decrease per patient from 0.99 to 0.42 in hospital admissions, from 0.99 to 0.37 in ED visits, and a reduction of 9.7 hospitalization days per year (P<.001). Additional analyses stratifying by hospitalization history, disability level, and caregiver status showed that the greatest effect of the RPM system was on patients with high risk and severe disability. Finally, there was no observed increase in mortality or transfers to intensive care units. Our findings are consistent with our previous results regarding the potential benefits of the EPOCA RPM system in managing care for polypathological older adult patients, this time with general practitioners as system operators. They also support existing evidence on the promise of RPM in improving care and health outcomes for older adult patients while alleviating hospital burdens by reducing unplanned hospitalizations and ED visits. It is, therefore, essential to incorporate reimbursement policies for these RPM initiatives so as to facilitate their adoption within health care systems and enhance their impact on health outcomes.

Chronic diseases, such as type 1 and type 2 diabetes, asthma, and chronic obstructive pulmonary disease , demand long-term treatment and permanent adaptation. One important pillar in coping with these diseases is individuals' self-management, including support from digital media. Research on their effects confirms their potential. However, it is flawed by theoretical underdevelopment and methodological weaknesses, such as a focus on short-term effects, single digital features, and microlevel studies. The research unit (RU) DISELMA (\"Digital Media in Chronic Disease Self-Management\") aims to examine the continued use patterns and effects of the digital self-management of chronic diseases, as well as the role of the interpersonal, organizational, and societal levels to gain a comprehensive picture of the individual processes, their contextual embeddedness, and cross-level interactions. To fully capture the manifold multilevel influences, the RU comprises 6 individual projects (IPs), each of which conducts several studies. Two projects at the individual level analyze determinants of use, usage patterns, and effects of digital media, combining systematic reviews, experience sampling method studies, focus groups, panel surveys, and content analysis of apps used. Two projects examine the interpersonal context by analyzing the role of health care providers and the diffusion of digital media in informal networks, conducting a scoping review, online surveys with physicians, semistructured interviews, and participant observations of physician-patient dyads, patient focus groups, and interviews with peers. One project aims to analyze the role of organizations within the mobile health market by conducting a content analysis of organizational messages and a survey. Finally, one project analyzes journalistic and social media to gain insight into the discourses about digital chronic disease self-management on the societal level. The RU received funding approval from the Deutsche Forschungsgemeinschaft (German Research Foundation; grant 456132969) in July 2023, and the 4-year funding period ranges from December 2023 to November 2027. IP1 is currently conducting its systematic reviews and experience sampling method studies, both to be finalized in 2026. IP2 is conducting its systematic review and meta-analysis alongside panel surveys until June 2026. IP3 has completed its online survey with physicians and is currently conducting observations until August 2026. IP4 is conducting its scoping review and peer interviews through 2026, while IP5 is working on its content analysis and survey, and IP6 on its manual content analysis. First publications of the results are expected in 2026. The results will contribute to the existing research through a theoretically and methodologically comprehensive approach that improves our understanding of the processes within and between all levels. These insights will inform providers of digital health solutions and health care practitioners about users' needs, advance evidence-based disease self-management programs, and contribute to better coping with chronic diseases, improved well-being of affected individuals, and reduced health care costs.