Evaluating the use and adaptation of implicit attitude measures in older adults: a scoping review protocol

The ongoing demographic shift toward an aging global population has significant implications for the design and application of psychological research tools. Among these, implicit attitude measures-used to assess automatic, unconscious evaluations-pose particular challenges when applied to older adults. Although such measures are widely used in psychological and health-related research, they were primarily developed and validated with younger populations. Age-related cognitive and sensory changes may interfere with task performance and compromise the validity of these tools, yet little is known about how such issues have been addressed or adapted for in empirical studies.

This scoping review will follow the Arksey and O'Malley framework with enhancements from Levac et al. We will systematically search five databases (PsycINFO, PubMed/MEDLINE, Scopus, Web of Science, and AgeLine) for studies published since 2000 that use implicit attitude measures with participants aged 60 and older. Eligible studies must report original empirical data and use indirect tasks (e.g., Implicit Attitude Test, Affect Misattribution Procedure, and Go/No-Go Association Task) to assess attitudes. Two reviewers will independently screen studies and extract data on measure type, task adaptations, and reported psychometric properties. Data synthesis will include descriptive statistics and thematic analysis organized around methodological domains.

This review will provide a comprehensive synthesis of how implicit attitude measures have been used, adapted, and evaluated in studies involving older adults. The findings will inform best practices for designing more age-appropriate, valid tools for research involving aging populations. As the use of implicit measures expands, ensuring their applicability across demographic groups is critical for inclusive and accurate psychological assessment.

Open Science Framework registration number https://osf.io/r8smx/.