JMIR Publications

ABSTRACT

Given the diversity of human characteristics and experiences, personalizations (e.g. in nudges, messages, choice presentations, health-medical interventions, designs) have been increasingly adopted in digital health platforms to promote engagements more effectively. Psychology often uses long self-report scales to measure various psychological attributes. Although they are useful in tapping into individuals’ psychological profiles, when applied in real, everyday settings to assess individual differences, people are most likely unwilling to complete them. With the pressing need to personalize digital health platforms to enhance retention and engagement, ultra-short versions of these psychological scales are needed to allow assessment of multiple attributes at the same time. We encourage regression analyses of each item, factor analyses, item response theory, ant colony optimization, and/or machine learning methods to empirically shorten psychological scales to facilitate personalization.