JMIR Aging最新文献

Background: Estimated pulmonary biological age (ePBA) has emerged as a more reliable indicator for disease progression and mortality than chronological age, with chest CT as a promising tool for calculating ePBA. However, the lack of models trained and validated with large-scale healthy adults hinders the generalizability of the CT-based ePBA.

Objective: To develop an ageing biomarker (ePBA) from multi-center chest CTs of healthy adults using deep learning and investigate the association between age gap (ePBA－chronological age) and pulmonary function as well as all-cause mortality in patients with chronic obstructive pulmonary disease (COPD).

Methods: We utilized 11,187 chest CT scans from healthy adults at three health management centers and used multiple deep-learning models. Of these, 7,726 scans from Institution A were used for model development. The remaining CT scans from Institution B (n=1,506) and C (n=1,955) served as external test datasets. To examine whether ePBA provided information beyond chronological age in patients with the disease, we investigated the association of age gap with lung function and all-cause mortality among 138 COPD patients hospitalized at the same time period in institution A.

Results: The deep learning models demonstrated acceptable applicability for this task, exhibited a strong correlation between ePBA and chronological age. Age gap was significantly associated with FEV1% reduction (rs=-0.18, P=.03) and an increased risk of all-cause mortality (HR: 1.16 [ 95%CI: 1.08, 1.25]) in patients with COPD.

Conclusions: This study developed and validated the biomarker of ageing-ePBA- with deep learning models based on chest CT. Age gap could serve as a novel clinical biomarker in COPD patients.

Clinicaltrial:

Background: The therapeutic efficacy of acupuncture in treating Alzheimer disease (AD) largely depends on consistent treatment adherence. Therefore, identifying key factors influencing adherence and developing targeted interventions are crucial for enhancing clinical outcomes.

Objective: This study aims to develop and validate a predictive model for identifying patients with AD who are likely to maintain good adherence to acupuncture treatment.

Methods: This secondary analysis included 108 patients with probable AD, aged 50 to 85 years, from 2 independent randomized controlled trials conducted at Guang'anmen Hospital, China Academy of Chinese Medical Sciences. Of all, 66 patients were assigned to the development cohort and 42 to the external validation cohort. Acupuncture adherence was defined as the proportion of completed sessions relative to scheduled sessions, with good adherence defined as ≥80% completion. Baseline data included demographic, clinical, cognitive, functional, psychological, and caregiving variables. Multivariable logistic regression with backward stepwise selection was used to identify significant predictors, and a nomogram was constructed based on the final model. Model performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis, with external validation performed by receiver operating characteristic analysis. Sensitivity analysis was performed using alternative adherence thresholds of 70% and 90%.

Results: A higher number of treatments during the first month was associated with a significant increase in the odds of good adherence (odds ratio [OR] 3.06, 95% CI 1.68-7.01; P=.002), while longer disease duration (OR 0.97, 95% CI 0.94-1.00; P=.049) and receiving care from a part-time caregiver (OR 0.19, 95% CI 0.04-0.72; P=.022) were associated with lower odds of adherence. Sensitivity analyses further supported the stability and reliability of the model.

Conclusions: This study is the first to develop and validate a predictive model for acupuncture adherence in patients with AD. In clinical research, it can facilitate participant stratification and help identify individuals who may need additional adherence support, thereby reducing bias and enhancing trial quality. In clinical practice, the nomogram enables proactive adherence management by prospectively identifying high-risk patients and guiding targeted strategies to improve adherence and optimize therapeutic outcomes.

Background: As the global aging population accelerates, mobile health (mHealth) apps have emerged as critical tools in the health management of older people. However, the promotion of mHealth apps has faced multiple obstacles, including insufficient technological adaptation to aging, digital resistance, and ageism. The impact of ageism on technology usage experiences among older adults is influenced by mechanisms such as stereotypes and biases. Notably, extant research has not adequately explored the subjective experiences of older adults in the context of mHealth app usage scenarios.

Objectives: The present study was predicated on the extended unified theory of acceptance and use of technology model and the risks of ageism model to systematically explore and understand older adults' ageism experiences in mHealth app usage. Our objectives were to provide a reference for optimizing age-friendly design and enhancing digital health management capabilities for older adults.

Methods: This qualitative study utilized an interpretive phenomenological design and was conducted between February and April 2025. Purposive sampling was employed to select older adults with experience using mHealth apps in a Shanghai community for semistructured interviews. This study used Colaizzi's phenomenological method to analyze and summarize older adults' experiences and perceptions of ageism and to extract themes.

Results: The study identified 3 core themes: (1) internalized age stereotypes, which manifest as technological uselessness and learning barriers; (2) anxiety and avoidance behaviors caused by stereotype threat; and (3) external unfair treatment (such as age-friendly design flaws and inadequate support systems), which inhibits usage. These experiences significantly impact older adults' intention to use mHealth apps.

Conclusions: Ageism profoundly affects the engagement of older adults with mHealth apps. It is advisable to execute systematic interventions to improve digital inclusion and health self-management capabilities, including strategies to challenge age stereotypes, optimize intergenerational support, refine age-friendly design, and establish strong social support networks.

Background: Undiagnosed cognitive impairment poses a global challenge, prompting recent interest in ultra-brief screening questionnaires (comprising <2 to 3 items) to efficiently identify individuals needing further evaluation. However, evidence on ultra-brief questionnaires remains limited, particularly regarding their validity across diverse literacy levels.

Objective: This study aimed to develop an ultra-brief questionnaire that performs well in detecting mild cognitive impairment or dementia (MCI/dementia) across diverse literacy levels and to compare its performance with an established questionnaire (the 8-item Informant Interview to Differentiate Aging and Dementia [AD8]).

Methods: This diagnostic study involved 1856 participants aged ≥65 years (median education 10 y, range 0-23 y), prospectively recruited from community settings in Singapore. Participants and informants completed 15 cognition-related questions. MCI/dementia was diagnosed via a comprehensive assessment and consensus conference. The sample was randomly split 70/30-the training sample (70%) was used to derive an ultra-brief questionnaire from the 15 cognition-related questions (using an exhaustive search approach), and the test sample (30%) evaluated the area under the receiver operating characteristic curve (AUC).

Results: The new questionnaire comprised 2 informant questions (ie, assistance with medications and worry about cognition), plus age and years of education. It demonstrated excellent performance in detecting MCI/dementia (AUC 85%, 95% CI 80%-90%), significantly better (P=.003) than a nested baseline model (comprising age and years of education; AUC 78%, 95% CI 73%-83%). In contrast, the AD8 had an AUC of 76% (95% CI 70%-83%), not significantly different (P>.99) from the baseline model. The questionnaire's performance was consistent across education subgroups and varying prevalence scenarios. Two optimal cutoffs were used-the lower cutoff provided 80% sensitivity and 96% negative predictive value, and the upper cutoff provided 99% specificity and 81% positive predictive value. A web-based calculator is available for public use.

Conclusions: This ultra-brief questionnaire enables rapid screening for cognitive impairment (in <1 min) by family members or as part of community geriatric assessments. Its excellent performance across literacy levels supports its utility for case finding in diverse populations, including underserved communities and lower- and middle-income countries.