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Can We Reverse Our Psychological Age And Get Happier At The Same Time Using Artificial Intelligence?

“…we can generate preferences in older people that resemble younger people when we expand their theoretical time horizons.” — Dr. Laura Carstensen, Professor of Psychology, Stanford University

“We can view happiness in at least three ways—as a hedonic state, as a cognitive state, or as a general life philosophy. Happiness, then, can refer to a way of thinking, such as being optimistic; a way of feeling joy, pleasure, relief, or gratitude; or simply a way of being.”— Dr. Nancy Etcoff, Professor of Psychology, Harvard Medical School

Last week, a paper I co-authored with the scientists from Deep Longevity, Chinese University of Hong Kong, and Stanford, went viral and was covered in Fortune, Fox News, The Hill, New York Post, The Guardian and many other top-tier international media. In this study, the team used AI to analyze the psychological and biological changes during aging. This was the fourth peer-reviewed paper that tried to use AI to understand the changes in human psychology that transpire during aging but the first one to link biology with psychology. One day I will tell the personal story why a group of hardcore scientists studying biology and chemistry using AI decided to venture into psychology but these studies convinced me that human psychological aging is much more important than most scientists in biological sciences tend to believe. I firmly believe that the psychological age defines us much more than the biological age even though these ages, as this recent study showed, are closely connected. And as Laura Carstensen showed in her pioneering work on the Socio-Emotional Selectivity Theory (SST), psychological aging is plastic – we have the toolkit to modify it. And the recent study on aging and happiness demonstrated that it may be possible to get psychologically younger and happier at the same time. The results were so convincing that I started using some of these tools myself. Let’s take a deeper dive into how research in the psychology of aging and AI tools can help us adjust our psychological age and improve our well-being.

Longevity Mindfulness: How Can Artificial Intelligence Help You Better Understand Yourself?

Age is much more than the number of times you saw Earth’s orbit around the sun. As the body grows old, our minds are often instigated by mental decline and progressive, natural psychological aging. People’s bodies age at different rates, and both mindset and environment shape and defy their aging dynamics. Importantly, our psychological state influences our perception of time and health status. Also known as subjective age, an individual’s psychological age is based on how young or old one perceives themselves to be in comparison to their chronological age, depending on an individual’s self-assessment of the degree of aging, and how this perception influences their well-being in general.

Years of research on well-being and longevity have shown that psychological well-being is tightly linked to physical health, mood variations, and supportive social networks. As we grow older, our mental state starts stabilizing, with negative emotions being more easily managed, and our social networks becoming smaller, as we search for deeper connections with a small group of friends. In parallel, our sense of psychological well-being and life satisfaction becomes greater, given an accumulated lifetime of experiences dealing with adversity and daily hassles.

One theory that fairly explains this changing time perspective was proposed by Stanford’s Dr. Carstensen, the socio-emotional selectivity theory, whereby motivation and goal pursuit by younger people, that have a lifetime ahead of themselves, is related to the acquisition of knowledge (for example, taking a university degree). In contrast, in older adults, when time is perceived as limited, emotional-based goals take priority.

Most typically addressed as the answer to “How old do you feel?”, we constantly find ourselves at internal crossroads striving to better understand ourselves and our inner states and to manage our and others’ expectations in terms of natural, “healthy” aging. But… can we anticipate how long we’ll live? Can we develop a longevity-oriented mindset rather than a failure one and prepare for a longer, more enjoyable, and more productive lifespan? Are there any tools to help us figure out better prospects and sharpen our perception of life?

While much progress has been made during the last decades regarding the identification of markers used to assess biological aging, including those based on biochemical, transcriptomic, proteomic, epigenetic, and microbiome-related signatures, these do not accurately translate one’s state of psychological aging. Although the discovery of such markers has given great promise regarding the development of therapeutic intervention strategies, knowledge about the factors that modulate one’s subjective age is still missing as well as potential therapeutic avenues. As subjective age, itself can be influenced by various parameters, including personal experiences, social relationships, and cultural values, identifying biomarkers and designing tools that can measure changes in one’s psychological age is much needed.

Over the last couple of decades, the boom in artificial intelligence (AI) technologies, namely in machine learning algorithms and deep learning techniques, has allowed the identification of meaningful patterns in human behavior and psyche. Its crossover with the fields of psychology and longevity granted the opportunity to envision neural networks capable of creating digital models of aging — aging clocks — that can capture the influence of multiple aging processes (e.g., DNA methylation) and possible outcomes (e.g., mortality risk) to predict human chronological age.

Exciting research on AI-based engines sheds light on novel tools that can estimate one’s psychological age and future well-being based on a constructed psychological survey. Basically, these AI tools can be used to determine ways of improving and maximizing well-being long-term based on inferred information from available datasets. Using data from the Midlife in the United States (MIDUS) dataset, two models have been previously designed, PsychoAge and SubjAge, to project chronological and subjective age, respectively, in a robust, validated and predictive manner across different age groups and taking into consideration 50 psychosocial features. Both clocks contained actionable features that could be modified using social and behavioral interventions and, importantly, that may prove to be useful in shifting personal perception of aging towards a mindset that promotes productive and healthy behaviors.

More recently, using the same dataset, the Deep Longevity team proposed a new model that works as a recommendation engine that advises a person seeking self-improvement based on its neuropsychological characteristics. In technical terms, it is an unsupervised learning algorithm that integrates the information from a respondent, places it on a 2D map of all possible psychological profiles (i.e., psychotypes), and then derives suitable ways to improve current and future psychological well-being based on self-organizing maps (SOMs). Although we have previously released Young.AI App, an AI platform to keep track of the aging process, Deep Longevity has now elevated its findings and released a web service FuturSelf, a free online application that provides a report with insights aimed at improving users’ long-term mental well-being based on their responses to the psychological test described in the original publication. Participants can actually enroll in a program that provides guidance based on AI-chosen recommendations, which I find really exhilarating. This novel tool shows great promise because it provides unparalleled personalization supported by scientifically sound data while offering an exciting perspective on psychological aging, long-term well-being, and risk of age-related disorders. It further demonstrates the application of machine learning approaches to the issues of psychological health. Right now, the team is working on a follow-up study on the effects of happiness on physiological measures of aging.

“Older” Psychological Age May Signal Problems

As life expectancy in developed countries has been steadily increasing during the last century, several studies have also suggested that a lower (or younger) subjective age is associated with better mental and physical health, cognitive and emotional processing, and overall increased well-being and life satisfaction. In other words, feeling mentally younger than one’s chronological age is considered a protective factor to buffer against the negative impact of aging and all its associated perks. Perceiving oneself as subjectively younger than one’s chronological age may influence several age-related biological changes. Conversely, a higher (or older) subjective age is significantly associated with increased frailty and decline in healthy behaviors, mental health, and survival rate. Interestingly, people with an older subjective age have been shown to have higher levels of systemic inflammation, higher risk of obesity, pulmonary and muscular dysfunctions as well as increased incidence of certain diseases. Biological correlates of this phenomenon have also shown that subjective aging is associated with health-related problems. Indeed, previous studies have found an association between older subjective age and higher levels of albumin, systemic inflammation and obesity. In line with this, perceived older age has been associated with certain diseases such as diabetes and, more importantly, psychiatric disorders. This links well with the fact that people with mental disorders have been found to be at greater risk of aging faster and developing age-related diseases, showing that mental state and perception may accelerate aging effects. Stress is a well-known triggering factor for mood swings and a key modulator of the pace of aging. Accordingly, chronic stress is a key source of psychiatric manifestations, namely depression, and unhappiness and may as well greatly impact the way one perceives aging effects.

The rise in life expectancy has led to substantial variability in one’s assessment of psychological age, with individuals perceiving themselves and others as substantially younger or older than their actual chronological age. This shift in the perception of subjective age has profound effects on behavior and well-being, and is linked to an individual’s mindset and lifespan progression. Subjective age is by itself a significant predictor of many psychological factors among older adults. Indeed, as I previously stated, research shows that individuals with younger subjective age display a tendency to have better mental health. Thus, tools that measure, analyze, and predict psychological aging as well as methodological approaches that modulate longevity expectations and psychological aging states are important to improve perceived health status and general well-being.

Can Psychological Age Reversal Make You Unhappy?

One important aspect about predictive digital tools (the aging clocks abovementioned) is that they can be “reversed” using social and behavioral interventions. But, although I have previously given 10 tips to reverse your psychological age (from forming meaningful social connections to learning a new foreign language), this may not be ideal in every scenario. While there is a consensus among longevity researchers that preventing some biological features from entering senescence may revert some aspects of physiological aging, things are much different on the realm of psychological aging. As we age at different rhythms, both physically and mentally, it may be difficult to find the correct way to rejuvenate your psychological age. Moreover, since we get “happier” as we grow old, to better cope with the prospect of imminent decline and death, employing strategies to reverse or rejuvenate your psychological age in an uncontrollable fashion may actually lead to reduced well-being. In fact, new experiments are now being designed to employ different strategies to rejuvenate older adults and change their mindset and perceptions using counterclockwise psychological interventions.

Feeling a ‘young spirit’ in an older body may not be that great. In my opinion, a tight balance between your chronological and biological age with your psychological age needs to happen. On one hand, there is a demand for tools that determine an individual’s exact biological age — which are already available in the market — and, on the other hand, mechanisms in place to infer one’s psychological age and match it accordingly. Installing AI-based models and tools capable of finetuning that balance is crucial to improve the future of mental health.

Indeed, recent AI technology has given a glimpse of this possibility. It has provided a means to find the optimal path to emotional stability. As mentioned before, this new deep learning model can predict one’s current psychological age and guide future well-being, providing personalized recommendations for optimizing one’s mental resilience. The SOM built within the model offers a set of non-trivial, personalized paths towards improved well-being that can be utilized as a reference for purposes of cognitive behavioral therapy and online mental health approaches. Perhaps, this tool can be used as a complement or even as a standalone approach to adjust one’s psychological age and provide emotional improvement and mental steadiness. Not only is it transversal to different areas of research, including psychology, psychiatry, longevity, and gerontology, but it may also be used in several applications in both medical and biotechnology industries.

Recovering Psychological Youthfulness After the COVID-19 Pandemic

Several recent reports show that the COVID-19 pandemic has exacerbated the mental health situation worldwide. Growing psychological distress, boredom, and feelings of isolation and loneliness have led to a decrease on individual well-being that is hampering society’s productivity and ability to endure these challenging times. Most likely, during COVID-19 outbreak’s initial stages, people became less optimistic and psychologically older. While in the US life expectancy in 2021 dropped by 1.6 years due to COVID-19, suggesting its toll on both physical and psychological health, China’s zero-COVID policies have substantially averted surges in mortality, surpassing the life expectancy rates in the US, despite Chinese data for 2021 not being yet available. Overall, many people became less optimistic due to constant lockdowns and the inability to travel and meet new people. The lack of freedom and closedness most likely worsened subjective aging in several groups of people, especially those more susceptible to the effects of environmental factors or with a greater socio-emotional burden. Surprisingly, indicators of relative biological and psychological aging of certain individuals were found to affect the frequency and severity of the disease, with the combination of high indicators of biological age and underestimated psychological age dangerously increasing the likelihood of developing severe forms of the disease.

But, as I’ve addressed before, older adults may be more resilient to the influence of stress and aging, thus, less likely to suffer from psychological aging effects. In line with this, studies suggest that older age may indeed buffer against the COVID-19-related impact on mental health, although the long-term consequences may vary across countries and cultures. Findings highlight the link between positive coping behaviors and psychosocial well-being and indicate that older adults may use unique adaptive mechanisms, yet to be uncovered, to preserve well-being during the COVID-19 pandemic. That is why it is important continuous research into psychological aging, measures of happiness and the development of new tools that may help reverse psychological aging and improve well-being.

Hence, it is for current research on AI, psychological aging and longevity to harness the potential of machine learning and digital tools for a safe and modern future that relies on models and approaches such as the ones previously mentioned to improve psychological health and well-being on a self-guided level or in a therapeutical/interventional context. The emergence of tools that can precisely estimate psychological aging, predict disorder trajectory, identify changes in an individual’s behavior, and provide data that informs personalized medical care is ever more promising and will revolutionize the future of human performance and digital health.

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