{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:33:49Z","timestamp":1790696029633,"version":"4.1.0"},"reference-count":63,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Self-rated health is associated with information and communications technology (ICT) use among older adults. Non\u2013US born, older Asian American individuals are more inclined to rate their health as fair or poor compared to individuals from other racial and ethnic backgrounds. This population is also less likely to use ICTs as compared to White older Americans. Furthermore, cognitive decline may impact technology acceptance. In a previous adaptation of the technology acceptance model for low-income, Asian American older adults, perceived usefulness (PU), perceived ease of use (PEOU), age, educational attainment, ethnicity, and English proficiency were significant predictors of ICT use. However, the association between health and technology acceptance has not been explored among Asian American older adults.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study examined the role of self-rated health and subjective cognitive decline in the acceptance and use of ICTs among low-income, Asian American older adults.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This cross-sectional survey included Asian American individuals aged \u226562 years living in affordable housing for older adults (N=392). Using hierarchical multiple regression, we explored the association between self-rated health and ICT use and technology acceptance model mediators (PU and PEOU) while adjusting for demographics, English proficiency, and subjective cognitive decline. Contrast statements were used to estimate contrasts of interest. To further examine the separate and joint association between age and subjective cognitive decline and the dependent variables, we examined scatterplots with locally estimated scatterplot smoothing lines, revealing that the relationship between subjective cognitive decline and ICT use varied in 3 age segments, which led to updating our analysis to estimate differences in ICT use among age categories with and without subjective cognitive decline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Self-rated health was not significantly associated with ICT use (\u03b2=.087; P=.13), PU (\u03b2=.106; P=.10), or PEOU (\u03b2=.062; P=.31). However, the interaction terms of subjective cognitive decline and age significantly improved the model fit for ICT use (\u0394R2=0.011; P=.04). In reviewing scatterplots, we determined that, in the youngest age group (62-74 years), ICT use increased with subjective cognitive decline, whereas in the older age groups (75-84 and \u226585 years), ICT use decreased with subjective cognitive decline, more so in the oldest age category. Through regression analysis, among participants with subjective cognitive decline, ICT use significantly decreased in the middle and older age groups as compared to the youngest age group. However, among participants without subjective cognitive decline, the difference in use among age groups was not significant.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study contributes to the understanding of the complex relationship between health and ICT acceptance among low-income, Asian American older adults and suggests the need for tailored interventions to promote digital engagement and quality of life for this population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/57009","type":"journal-article","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:44:54Z","timestamp":1730162694000},"page":"e57009","source":"Crossref","is-referenced-by-count":4,"title":["The Role of Health in the Technology Acceptance Model Among Low-Income Asian American Older Adults: Cross-Sectional Survey Analysis"],"prefix":"10.2196","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9983-4114","authenticated-orcid":false,"given":"Pauline","family":"DeLange Martinez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3884-7907","authenticated-orcid":false,"given":"Daniel","family":"Tancredi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8023-6405","authenticated-orcid":false,"given":"Misha","family":"Pavel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9976-761X","authenticated-orcid":false,"given":"Lorena","family":"Garcia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1691-3993","authenticated-orcid":false,"given":"Heather 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