Search Articles

View query in Help articles search

Search Results (1 to 10 of 684 Results)

Download search results: CSV END BibTex RIS


Effectiveness of Walking Prescription Using Mobile Health Technology on the Changes in Daily Steps in Older Adults With Cognitive Impairment: Randomized Controlled Study

Effectiveness of Walking Prescription Using Mobile Health Technology on the Changes in Daily Steps in Older Adults With Cognitive Impairment: Randomized Controlled Study

However, many studies on PA programs using m Health technology have mainly been conducted on young people [19-22]. Although it has also been suggested that PA can be increased in the older population using m Health technology [23], most studies have been conducted on older adults with physical diseases such as cardiovascular disease, diabetes, and obesity without cognitive dysfunction [23-25].

Hee Jung Kim, Yun Jung Hwang, Jee Eun Park, Dong Young Lee

JMIR Aging 2025;8:e63081

Digital Literacy and Its Association With Subjective Health Status and Healthy Lifestyle Behaviors Among Korean Older Adults: Cross-Sectional Study

Digital Literacy and Its Association With Subjective Health Status and Healthy Lifestyle Behaviors Among Korean Older Adults: Cross-Sectional Study

Digital technology initially spread among young people, men, and highly educated people and is gradually spreading to older adults, women, and less educated people. The aging trend and the rapid development of new media technology suggest that older adults will have to use digital technology more in the future [9]. Digital literacy comprises the ability to understand the information generated on a digital device and implement it accordingly [10].

Soon Young Lee, Yejin Kim, Bomgyeol Kim, Sang Gyu Lee, Suk-Yong Jang, Tae Hyun Kim

JMIR Aging 2025;8:e64974

Digital Health Interventions Targeting Psychological Health in Parents of Children With Autism Spectrum Disorder: Protocol for a Scoping Review

Digital Health Interventions Targeting Psychological Health in Parents of Children With Autism Spectrum Disorder: Protocol for a Scoping Review

Reference 12: Meta-analysis of parent-mediated interventions for young children with autism spectrum Reference 19: Annual research review: digital health interventions for children and young people with Reference 24: Internet-based versus face-to-face intervention training for parents of young children Reference 48: Telehealth training in principles of applied behavior analysis for caregivers of young

Binbin Ji, Intan Maharani Sulistyawati Batubara, Janene Batten, Xinyi Peng, Sanmei Chen, Zhao Ni

JMIR Res Protoc 2025;14:e68677

Deep Learning–Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study

Deep Learning–Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study

Previous studies have demonstrated an independent association between FEV1 and the risk of AE-COPD in both young and old patients with COPD [6,7]. FEV1 is a key predictor of AE-COPD in the majority of existing prediction models [8]. However, its association with exacerbations is relatively weak, limiting its role as a predictive factor [9].

Eun-Tae Jeon, Heemoon Park, Jung-Kyu Lee, Eun Young Heo, Chang Hoon Lee, Deog Kyeom Kim, Dong Hyun Kim, Hyun Woo Lee

J Med Internet Res 2025;27:e69785