ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2025
Tailored Approach to Designing a Digital Research Platform for Adults Aged 95 and Older: SuperAgers Family Study.
Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe SuperAgers Family study aims to investigate phenotypic and genetic mechanisms related to healthy aging in nonagenarians, centenarians, and their family members. A remote study design was tested to demonstrate the feasibility of using digital technology to conduct health research within this rare population of advanced age. This paper describes key design elements of the digital research platform developed to deliver consent, enrollment, and study data collection in a cohort of older adults.
methodsSuperAgers participants aged 95 years or older, their offspring, and offspring's spouses were invited to join the study via media and community outreach. Participants completed registration, consent, submitted study data, and completed remote biospecimen collection via the web-based study app. Platform design elements and functionality were adapted for use by older-aged adults. Qualitative process evaluation assessed usability and participant data entry completion throughout the study workflow.
resultsPreliminary data from SuperAgers (n = 160) of average age 98 years (±3 standard deviation [SD]) and offspring/spouses (n = 127) of average age 69 years (±5 SD) were evaluated. About 97% of participants in both groups successfully used the platform to complete eligibility screening, eConsent, and study surveys.
conclusionsSuperAgers and offspring successfully used the digital research platform to complete eConsent and submit study data. This supports the feasibility of conducting digitally enabled research in older-aged populations using tailored platform design elements that increase usability and minimize entry errors. These findings may contribute to the development of best practices for digitally delivered research studies in aging populations.
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