The Johns Hopkins Undergraduate Summer Training and Research Program (Hopkins USTAR) was a 2-year program funded by the National Institute on Aging R25 Advancing Diversity in Aging Research through Undergraduate Education. Hopkins USTAR was designed to provide undergraduate students primarily from underrepresented backgrounds in biomedical and behavioral science with interdisciplinary research training in aging and health disparities research. From 2023 to 2025, a total of 15 undergraduate students participated in faculty-mentored research projects, professional development, research education, and national research conferences. Program evaluation methods included pre- and post-program surveys and interviews. Results suggest that the Hopkins USTAR program significantly increased aspects of participants' research self-efficacy and strengthened perceived science identity. Despite the early termination of Hopkins USTAR, the program's short-term outcomes indicate modest success in fostering positive feelings about science and preparing participants for graduate school. Continued follow-up to assess long-term program outcomes will be important to measure impact.
The Insomnia Severity Index (ISI) is a brief screening tool that assesses the presence and severity of insomnia symptoms. Although widely validated, its psychometric properties remain understudied in older adults, a population at increased risk for insomnia due to age-related changes in sleep architecture and quality. This study assessed the concurrent and factorial validity of the ISI among community-dwelling older adults (N = 250) who completed the ISI and measures of comorbid symptoms: Brief Geriatric Suicide Ideation Scale, Michigan Alcohol Screening Test-Geriatric Version, Geriatric Depression Scale-15, and Geriatric Anxiety Scale. Bivariate correlations supported concurrent validity, with insomnia positively associated with anxiety, depression, suicidal ideation, and alcohol misuse. Anxiety was the strongest correlate and the only unique predictor of ISI scores in multivariate analyses. Exploratory factor analysis revealed a one-factor solution. Findings support the concurrent validity and factor structure of the ISI and highlight comorbidity with other mental health symptoms among older adults.
The diffusion of generative artificial intelligence is redefining the relationship between older adults and digital technologies. Existing studies have largely explained the digital divide among older adults through traditional digital media such as smartphones and mobile payment, while insufficient attention has been paid to how conversational artificial intelligence changes older adults’ entry thresholds, sustained-use motivations, and risk-judgment mechanisms. This study adopts a multimethod qualitative design, with semistructured interviews as the primary method and digital scenario-based tasks as a complementary method. From January to February 2026, the study was conducted in Chedun Town and Xinqiao Town, Songjiang District, Shanghai, with 40 participants aged 60 and above who had internet-use experience and showed a pronounced tendency toward high-frequency smartphone use. The data were analyzed through thematic analysis. Interview transcripts, task-observation records, screenshots of AI conversations, and field notes were triangulated; an initial coding manual, double review, and consensus discussion were used to enhance analytic credibility. The study finds that generative artificial intelligence shifts the technological threshold from procedural operation to need articulation and result evaluation, enabling some older adults to compensate for limitations in traditional interface operation through life experience and language-expression skills. Older adults’ sustained use is not driven simply by efficiency, but is closely related to emotional anchoring in later life, experience organization, relationship maintenance, and concrete task handling. Community support, peer learning, and intergenerational assistance constitute important mediating mechanisms through which older adults move from initial experimentation to stable use. Whether older adults truly cross the digital divide depends on whether they can develop the capacities of source verification, contextual delimitation, and boundary judgment. Based on these findings, the study proposes a “conditional crossing” mechanism for older adults’ acceptance of AI technologies and argues that age-friendly governance should move beyond interface simplification toward capability enhancement and risk support.
The following collection of papers represents some of the work produced by Advancing Diversity in Aging Research programs during the past few years. Some papers address training program outcomes, whereas other papers demonstrate the breadth and depth of scholarship produced by different training programs.
In this third Special Issue highlighting the efficacy and deliverables from NIA undergraduate Advancing Diversity in Aging Research programs, these six articles attest to both the training outcomes and the scientific knowledge that is gained by focused mentored training among undergraduates. We provide a few points of guidance for future R25 programs.
