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Development and Validation of a Predictive Model for Activities of Daily Living Dysfunction in Older Adults: Retrospective Analysis of Data From the China Health and Retirement Longitudinal Study

Development and Validation of a Predictive Model for Activities of Daily Living Dysfunction in Older Adults: Retrospective Analysis of Data From the China Health and Retirement Longitudinal Study

These factors reflect multidimensional contributions from psychosocial states, physical frailty markers, systemic inflammation, and biomechanical stressors. Notably, participants with a history of disability, chronic diseases, or falls exhibited significantly higher ADL dysfunction risk, consistent with previous evidence on functional decline pathways [14,15]. The 2.5-m walking time defined in this study is negatively correlated with gait speed.

Fangbo Lin, Chao Liu, Hua Liu

JMIR Med Inform 2025;13:e73030

Outdoor Exercise Facility–Based Integrative Mobile Health Intervention to Support Physical Activity, Mental Well-Being, and Exercise Self-Efficacy Among Older Adults With Prefrailty and Frailty in Hong Kong: Pilot Feasibility Randomized Controlled Trial Study

Outdoor Exercise Facility–Based Integrative Mobile Health Intervention to Support Physical Activity, Mental Well-Being, and Exercise Self-Efficacy Among Older Adults With Prefrailty and Frailty in Hong Kong: Pilot Feasibility Randomized Controlled Trial Study

Frailty affects various aspects of older adults’ lives, including gait, mobility, balance, muscle strength, motor processing, cognition, nutrition, endurance, and physical activity (PA) [4]. Importantly, high levels of frailty increase the risks of adverse health outcomes such as falls, hospitalization, and mortality [5]. Frailty is a modifiable, dynamic process characterized by frequent transitions between different frailty states over time.

Janet Lok Chun Lee, Arnold Y L Wong, Peter H F Ng, S N Fu, Kenneth N K Fong, Andy S K Cheng, Karen Nga Kwan Lee, Rui Sun, Hao Yi Zhang, Rong Xiao

JMIR Mhealth Uhealth 2025;13:e69259

An In-Person and Online Intervention for Parkinson Disease (UPGRADE-PD): Protocol for a Patient-Centered and Culturally Tailored 3-Arm Crossover Trial

An In-Person and Online Intervention for Parkinson Disease (UPGRADE-PD): Protocol for a Patient-Centered and Culturally Tailored 3-Arm Crossover Trial

Furthermore, all the aforementioned PD symptoms seem to relate to the reduction of physical activity, muscle mass, and strength levels, as well as movement performance of patients with PD, thus increasing sarcopenia and frailty [2-4]. It is well accepted that systematic physical exercise enhances neuroplasticity [5]; prevents or delays frailty [4,6]; and improves symptoms such as balance, gait, and Qo L in people with PD [7].

Michail Elpidoforou, Irene Grimani, Marianna Papadopoulou, Nikolaos Papagiannakis, Anastasia Bougea, Athina-Maria Simitsi, Evangelos Sfikas, Ioanna Alexandratou, Ioanna Alefanti, Roubina Antonelou, Christos Koros, Ioanna Mavroyianni, Chrysa Chrysovitsanou, Leonidas Stefanis, Daphne Bakalidou

JMIR Res Protoc 2025;14:e65490

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

Physical frailty and cognitive impairment are prevalent among older adults and have individually been associated with adverse health outcomes [1,2]. 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].

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

Assessing Social Interaction and Loneliness and Their Association With Frailty Among Older Adults With Subjective Cognitive Decline or Mild Cognitive Impairment: Ecological Momentary Assessment Approach

Assessing Social Interaction and Loneliness and Their Association With Frailty Among Older Adults With Subjective Cognitive Decline or Mild Cognitive Impairment: Ecological Momentary Assessment Approach

The co-occurrence of frailty and early-stage cognitive decline has been well documented, with studies showing an increased likelihood of frailty in older adults with SCD or MCI. Two systematic reviews and meta-analyses have offered solid evidence of this relationship [15,16].

Bada Kang, Dahye Hong, Seolah Yoon, Chaeeun Kang, Jennifer Ivy Kim

JMIR Mhealth Uhealth 2025;13:e64853

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

Recent evidence underscores the impact of neighborhood characteristics on frailty among older people. Those residing in neighborhoods with abundant green spaces exhibit a lower incidence of frailty, whereas individuals perceiving precarious conditions in their surroundings, houses, and environment face a higher risk of frailty [19-21].

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

JMIR Aging 2025;8:e64254

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

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

The prevalence of frailty is high among older adults aged ≥60 years [5]. Global frailty prevalence ranges from approximately 10% to 12% [6-11]. The percentage varies by age, gender, and frailty classification tool. In Thailand, frailty prevalence was 22.1%, which is twice the global frailty prevalence, according to the Thai National Health Examination Survey cohort in 2018. Specifically, Thailand’s northern region frailty prevalence was found to be 15% [12,13].

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

JMIR Aging 2025;8:e62942