Back

What content and methods are effective in developing cognitive enhancement programs for older adults with mild cognitive impairment? A cross-sectional study

kim, Y.; Kim, S.; Asami, K.

2025-12-29 nursing
10.64898/2025.12.20.25342600 medRxiv
Show abstract

Early intervention in older adults with mild cognitive impairment (MCI) is effective in maintaining and enhancing cognitive function. To maximize the effectiveness of such cognitive enhancement programs, it is essential to adequately reflect the characteristics and needs of the target population. This study aimed to assess the basic competencies of older adults with MCI, explore their needs and memorable experiences, and identify appropriate content and delivery strategies for cognitive enhancement programs. A cross-sectional design was employed, combining survey data and in-depth interviews. A total of 130 community-dwelling older adults with MCI living in both urban and rural areas of South Korea participated. Quantitative data included sociodemographic characteristics, cognitive function, health literacy, basic physical capability, and mobile device proficiency, and were analyzed using descriptive statistics, independent t-tests, and chi-square tests. Qualitative interview data were analyzed using inductive content analysis. The findings showed that participants had generally low levels of education, limited health literacy, and low mobile device proficiency, despite a high rate of smartphone ownership. Interview results revealed a strong demand for cognitive enhancement programs and suggested that participants preferred content related to family, recreational activities, career, academics, and Korean traditional holidays. These results underscore the need for cognitive enhancement programs that are tailored to the specific characteristics of community-dwelling older adults with MCI. Effective programs should take into account their lower levels of education, literacy, and mobile device proficiency, and incorporate content that reflects their needs and memorable experiences.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.