With the global population aging, and rising concerns about mental health, loneliness, and social isolation, understanding and enhancing later-life satisfaction has become increasingly crucial for both individual and global health and productivity. The World Health Organization1 reports that by 2030, one out of six people will be 60 years or older, comprising 1.4 billion people, with those over 80 approaching half a billion.

Given the vast scope of this issue, it's surprising that we have a limited understanding of what preserves and enhances generativity in our later years, as research to date is still early-on. Along similar lines, increased generativity would be expected to enhance well-being, and protect against many of the negative outcomes currently associated with aging. With the global population getting older and the average human lifespan increasing, it is imperative to work out how to extend healthspan and productivity, meaning, purpose and community.

A recent study by Mohsen Joshanloo, Ph.D., published in the Journals of Gerontology (2024), took a novel approach using machine learning to extract key variables from the Midlife in the United States (MIDUS) dataset. This study included a wide range of psychological and demographic variables and measured generativity using the Loyola Generativity Scale. Participants ranged from 39 to 93 years old, with an average age of 63.64. Using maching learning allows us to make sense out of complicated data sets where standard statistical approaches may falter.

From a broad perspective, a few key concepts help us understand aging across the lifespan.........

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