ArticleJMIR formative research2026
An AI-Assisted Cognitive Engagement Mobile App for Older Adults: Development and Mixed Methods Usability Study.
Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Alzheimer disease and age-related cognitive decline reduce memory engagement and limit caregiver insight, creating a need for accessible tools that support everyday cognitive activity in older adults. Although AI holds promise for personalized cognitive support, few AI-based apps have been developed and evaluated for memory engagement in this population, and fewer incorporate on-device emotional analysis with privacy-preserving design. Objective: This study developed and evaluated RecallLive, an AI-assisted mobile app supporting structured memory interactions for older adults, and examined its usability, engagement, perceived usefulness, and behavioral intention, guided by the Technology Acceptance Model. Methods: RecallLive integrates metadata-based photo clustering to generate memory videos, an on-device convolutional neural network that classifies frame-level emotional responses, and a large language model (LLM)-based module that converts these outputs into caregiver summaries. A sequential 2-phase mixed methods design was used. In phase 1, 202 US adults aged 65 years or older viewed a structured demonstration and completed a survey measuring ease of use, engagement, design clarity, perceived usefulness, and intention to use. In phase 2, 10 participants completed a hands-on session followed by semistructured interviews analyzed thematically. Quantitative analyses were conducted using Python (v.3.11; Python Software Foundation) and included internal consistency estimates; 2-tailed, 1-sample Results: All subscales showed strong reliability (Cronbach Conclusions: RecallLive was perceived as usable, engaging, and useful, with perceived usefulness strongly associated with adoption intention. In addition to efficiency, emotional relevance shaped engagement, particularly among participants with caregiving experience or personal connections to dementia. Future studies should use larger samples, longer direct-use periods, and longitudinal and clinical-integration designs to evaluate sustained use and cognitive outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.