We used behavioral intelligence insights focused on sociability, mobility, and independence in order to monitor the decline of the aging population for best care and resource allocation. Once an elderly person begins declining in one of these three areas, typically that indicates a turning point in their health moving forward.
Neura and Dan Ariely’s Kayma, teamed up to work with a world renowned humanitarian organization to run a data-driven analysis on the quality of life of the aging population during a global crisis, most notably one like the COVID-19 pandemic.
The purpose of the partnership was to understand the key indicators that lead to elderly decline, as well as specific cities in a designated region, that were most affected by this year’s pandemic. The goal moving forward was to then allocate the most appropriate government and nonprofit resources to those areas. Resources include, but are not limited to, elderly caretakers, physical health programs, adult daycare, shopping aids, and more.
In order to conduct the study, we looked at three key metrics in the aging population that have the most significant impact on elderly decline. The indicators are: independence, sociability, and mobility. These metrics were chosen because they are highly indicative of elderly health and share a direct correlation, positively or negatively, with the health and wellness of the aging population.
In partnership we looked at how metrics in these critical categories changed over time, specifically over the 2020 year.
By fusing real-world behavior data, movement patterns, sociability indicators and various other health and behavior indicators, Neura identified three key findings which were presented back to Health officials to be used when aiding the elderly population.
The invisible pandemic of senior loneliness prompted Neura and Kayma to use their joint resources to provide a solution to address senior loneliness and promote graceful aging.
While the COVID-19 pandemic instigated a clear need to monitor and understand the factors that contribute to aging, vulnerable populations have suffered great decline due to these three factors for decades.
In order to better assist with the vulnerable population, key questions were raised to guide the study. The questions had much to do with the pandemic’s effects on the aging population as well as the overall decline of the aging population as a whole irrespective of external factors: How could one measure the decline or turning point of an aging population, specifically one being rapidly threatened?
An investigation into measuring the decline in both mental and physical health examines the non-reversible effects of declining in age. Once the decline starts, it continues until death, with the individual becoming less independent, less social, and less capable of undertaking even basic daily tasks independently.
To understand how to prevent further and onslaught rapid decline, Neura and Kayma investigated human behavior, specifically zeroed in on a particular region looking at 80 cities, and their elderly populations change in sociability, independence, and mobility.
These key indicators were chosen because: loneliness is a disease that leads to heart problems and depression; decrease in physical activity; and the lack of independence, e.g. people who were doing things for themselves are now reliant on help for basic needs and day to day tasks, such as shopping trips to the supermarket, because stark changes in these factors are the first signs of decline.
Both Neura and Kayma provided real-world, real-life, real-time intelligence that resulted in accurate, updated answers in spaces where others run surveys to obtain retrospective aggregations. Traditionally, consultancies such as Accenture and McKinsey, provide insights, but they are delayed and often rely on survey data, whereas Neura runs the real-life intelligence – or the brainpower – behind the insights.
The behavioral analysis Neura does leads to a deeper understanding of the impact of the presented problems such as the decline in seniors’ health and how it affects movement, sociability, and population indicators in real-time. The ability to pinpoint where the impact was occurring: which cities, neighborhoods, etc., can be used to effectively deploy services, support, and additional resources for the elderly population.
Specific issues, such as where financial support is needed, what neighborhoods have more job losses than others, or people not traveling, etc., can be addressed directly. Real-time population gatherings and attendance in community centers show whether these centers are in high demand and why people are going there – be it for sociability or for food.
Neura uses quantifiable data to measure crucial aspects indicative of larger insights, such as the number of steps a person takes, which can be an activity gauge for overall health, movement, and independence. This shows someone’s change in mobility, showing when that declines, and thus leads to the greater decline of overall health.
Further these insights show the number of times someone leaves home and where they are going, and the social implications of their actions. If they’re going to the grocery store, it shows independence and that they are still able to provide for themselves, and heading to a community center indicates sociability. The distance traveled from home can show how independent someone is and how matriculated into society they are. Which answers the question of mental health and cognitive ability.
From studying these factors across the elderly population in the 80 cities Neura found three highly specific areas that required immediate further action and thus the utilization of the external tools specifically designated for elderly decline.
Findings include:
From these tangible findings, Neura was able to provide concrete information identifying the cities that experienced detrimental impacts on the aging population, exacerbated by the Coronavirus pandemic.
This information is vital for municipalities and government organizations to provide the specific services needed on a national level, while focusing resources where most needed in each individual community most struggling with graceful aging.
Our partnership didn’t end with these three conclusions. We aim in the next phase of this crucial project, to continue to work with, examine, and review Neura’s analytics dashboard which shares real-time information on the sociability, independence, and mobility of the aging population across the 80 cities in order to adjust, rework, and continue to provide specific, highly relevant, concrete initiatives and recommendations to relevant organizations in order to help minimize the detrimental impacts of elderly decline, and promote long, healthy, and happy lives for aging communities.
After working with 80 cities we found these key findings:
Identified 22 cities that showed a 50% change in leaving the house. Neura pinpointed cities that desperately needed extra food services, personal at-home checkups and calls
Pinpointed 16 cities that had a greater than 55% change in physical movement, while two cities showed extreme indications of complete lack of mobility.
Uncovered 15 cities with a greater than 40% drop in social interactions indicating loneliness.