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Sexual Response Problems and Their Correlates Among Older Adults From the Sexual Well-Being (SWELL) Study in China: Multicenter Cross-Sectional Study

Sexual Response Problems and Their Correlates Among Older Adults From the Sexual Well-Being (SWELL) Study in China: Multicenter Cross-Sectional Study

Given China’s rapidly aging population [24,25], the sexual health of older adults is a growing concern. A comprehensive understanding of older adults’ sexual response problems may enhance sex education, research, policy, and clinical care for this growing population. This multicentre cross-sectional study, using data from the Sexual Well-being (SWELL) study in China, aims to fill the research gap by examining the prevalence of sexual response problems and their correlates among older adults.

Bingyu Liang, Chen Xu, Bingyi Wang, Xinyi Li, Xin Peng, Ying Wang, Hui Li, Yong Lu, Xiaopei Shen, Lin Ouyang, Guohui Wu, Maohe Yu, Jiewei Liu, Xiaojun Meng, Yong Cai, Huachun Zou

JMIR Aging 2025;8:e66772

Perceptions of the Use of Mobile Apps to Assess Sleep-Dependent Memory in Older Adults With Subjective and Objective Cognitive Impairment: Focus Group Approach

Perceptions of the Use of Mobile Apps to Assess Sleep-Dependent Memory in Older Adults With Subjective and Objective Cognitive Impairment: Focus Group Approach

MCI refers to a transitional state between normal aging and dementia, where cognitive impairment is apparent, but daily functioning is largely intact. Our prior work demonstrated that individuals with multiple-domain MCI had significantly more compromised SDM than healthy controls and those with single-domain MCI [6]. In this study, poorer SDM was linked to having greater sleep apnea severity for older adults without MCI.

Aaron Lam, Simone Simonetti, Angela D'Rozario, David Ireland, DanaKai Bradford, Jurgen Fripp, Sharon L Naismith

JMIR Aging 2025;8:e68147

Applications of Self-Driving Vehicles in an Aging Population

Applications of Self-Driving Vehicles in an Aging Population

In this paper, we aim to provide an updated review of current and proposed applications of self-driving vehicles, particularly through the integration and application of mobility as a service to assist with prolonging the autonomy of aging persons, as well as review the limitations and future directions that have yet to be explored.

Sara Shu, Benjamin K P Woo

JMIR Form Res 2025;9:e66180

Relationship Between Within-Session Digital Motor Skill Acquisition and Alzheimer Disease Risk Factors Among the MindCrowd Cohort: Cross-Sectional Descriptive Study

Relationship Between Within-Session Digital Motor Skill Acquisition and Alzheimer Disease Risk Factors Among the MindCrowd Cohort: Cross-Sectional Descriptive Study

However, the role APOE ε4 in aging independent of AD pathology may be significant [33], as a small but growing body of evidence in both cognitively unimpaired humans and rodents shows that visual working memory and learning is better among APOE ε4 carriers than noncarriers [34-36]. Although APOE ε4 is the primary genetic risk factor for AD [13], evidence suggests a possible benefit, or compensatory behavior [37], of learning at an earlier age while leading to impairments in later life [38,39].

Andrew Hooyman, Matt J Huentelman, Matt De Both, Lee Ryan, Kevin Duff, Sydney Y Schaefer

JMIR Aging 2025;8:e67298

Association Between Sleep Duration and Cognitive Frailty in Older Chinese Adults: Prospective Cohort Study

Association Between Sleep Duration and Cognitive Frailty in Older Chinese Adults: Prospective Cohort Study

They often coincide with aging and can be bidirectionally linked to each other [3,4], prompting the introduction of the concept of cognitive frailty—the coexistence of both physical frailty and cognitive impairment [5]. The necessity is further justified by the findings that cognitive frailty poses an even greater risk of adverse outcomes compared to the isolated effects of the 2 conditions [6,7].

Ruixue Cai, Jianqian Chao, Chenlu Gao, Lei Gao, Kun Hu, Peng Li

JMIR Aging 2025;8:e65183

Correction: Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

Correction: Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

In “Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study” (JMIR Aging 2025;8:e62942) one error was noted. Reference 44 was a duplicate of reference 36, which reads as follows: Thinuan P, Siviroj P, Lerttrakarnnon P, Lorga T. Prevalence and potential predictors of frailty among community-dwelling older persons in northern Thailand: a cross-sectional study. Int J Environ Res Public Health. Jun 8, 2020;17(11):4077.

Natthanaphop Isaradech, Wachiranun Sirikul, Nida Buawangpong, Penprapa Siviroj, Amornphat Kitro

JMIR Aging 2025;8:e75690

Unveiling the Frailty Spatial Patterns Among Chilean Older Persons by Exploring Sociodemographic and Urbanistic Influences Based on Geographic Information Systems: Cross-Sectional Study

Unveiling the Frailty Spatial Patterns Among Chilean Older Persons by Exploring Sociodemographic and Urbanistic Influences Based on Geographic Information Systems: Cross-Sectional Study

Understanding the aging process and the sociodemographic determinants related to enhancing the quality of life has emerged as a very relevant research area in light of the rapid aging of the global population [1-3]. Currently, 12% of the world’s population is aged ≥60 years, and projections suggest that this proportion may rise to 21.5% by the mid century [4]. Similarly, the ≥80 years age group is expected to increase from 1.7% to 4.5% [4].

Yony Ormazábal, Diego Arauna, Juan Carlos Cantillana, Iván Palomo, Eduardo Fuentes, Carlos Mena

JMIR Aging 2025;8:e64254

Digital, Personalized Clinical Trials Among Older Adults, Lessons Learned From the COVID-19 Pandemic, and Directions for the Future: Aggregated Feasibility Data From Three Trials Among Older Adults

Digital, Personalized Clinical Trials Among Older Adults, Lessons Learned From the COVID-19 Pandemic, and Directions for the Future: Aggregated Feasibility Data From Three Trials Among Older Adults

Despite the presumed barriers to enrolling older adults in digital, remote clinical research, several trials conducted during the COVID-19 pandemic have succeeded in this pursuit, including a longitudinal brain aging study [20], a telemedicine initiative in a primary care setting [21], telehealth delivery of music therapy services [22], and a digital group intervention addressing worry and social isolation [23].

Lindsay Arader, Danielle Miller, Alexandra Perrin, Frank Vicari, Ciaran P Friel, Elizabeth A Vrany, Ashley M Goodwin, Mark Butler

J Med Internet Res 2025;27:e54629

Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study

Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study

This demographic shift toward an aging population has led to increased health care dependency and associated social costs. The medical industry related to aging and the social costs thereof are continuously increasing [2]. Accurately assessing biological aging is a critical first step in mitigating age-related diseases and their socioeconomic impact.

Chang-Uk Jeong, Jacob S Leiby, Dokyoon Kim, Eun Kyung Choe

JMIR Aging 2025;8:e64473