Color and Emotion in User Experience Design for Older Adults' Health Applications: A Multi-Database Bibliometric and Evidence-Mapping Study

Color is routinely present in health applications, but evidence about its role in older adults' user experience remains dispersed across design, health informatics, and human-computer interaction. This five-database study maps an affect-or-color focal corpus and applies a structured design-evidence audit to its color-term candidates. Searches of Web of Science Core Collection, Scopus, PubMed, IEEE Xplore, and the ACM Digital Library produced 2,515 source rows, 1,760 unique documents, and 941 focal records. A record-level audit of the classifier defining that corpus returned precision 0.880 and recall 0.982. Performance indicators, knowledge networks, and temporal bursts describe the corpus, and their stability was tested against retrieval, threshold, and periodization choices. Two design experts independently coded 41 color-term candidates, using full text for 23 records. Agreement reached 95.1% for inclusion (Cohen's kappa = 0.918) and 90.2% for population relevance (kappa = 0.853). Reconciliation retained ten primary older-adult studies, three mixed-age studies, and one contextual review; 27 records were excluded, and five of the ten color-and-emotion bridge records met the primary rule. Across the primary set, color supported legibility, risk communication, hierarchy, navigation, aesthetic preference, and affective tone, but most studies bundled color with other interface features. Preference-to-performance evidence was absent, and safety-relevant findings stayed embedded in broader workflows. The evidence map separates reported design functions from causal claims and identifies priorities for controlled validation.

@article{He2026,
  title     = {Color and Emotion in User Experience Design for Older Adults' Health Applications: A Multi-Database Bibliometric and Evidence-Mapping Study},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {9},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {YangFan He and Azizah Che Omar and Sobihatun Nur Abdul Salam},
  doi       = {10.14569/IJACSA.2026.0170930},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170930}
}