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Whitebook:
Retirement planning product based on biological age assessment

 

Date:  Sep 14, 2025
Version: 4

 

1. Introduction

 

1.1. Purpose of the document

 

This document is a White Paper on an innovative pension planning product based on the biological age of clients. The purpose of the document is to describe the product concept, its unique features, benefits and implementation approaches to ensure transparency and information for stakeholders.

 

1.2. Relevance of the approach

 

Traditional retirement planning systems are based on chronological age, ignoring individual differences in health and fitness.

  • Typically, pension service providers focus on standard age provided for by national legislation, to calculate the accumulation period, to estimate the amount of savings based on regular contributions and offer payment options, including annuities.
  • Biological age is an indicator that reflects the physiological state of the body, allowing for more accurate predictions of life expectancy and customer needs, making the service provider's offers more in line with reality.
    • There are many methods for assessing biological age (DNA, blood tests, etc.), most of which require physical contact and laboratory equipment.
    • New data sources, such as locomotive activity data stored on a smartphone, also serve as a reliable source of bioage.
  • The key characteristics of pension products are gender and age, which are used to select assumptions from mortality tables constructed from historical data.
  • Bioage reflects changes accumulated in the human body which can be measured:
    • If the biological age exceeds the calendar age, this can be interpreted as accumulated health problems and a higher rate of aging;
    • If the biological age is less than the calendar age, this indicates a healthier lifestyle and the likelihood of a higher life expectancy;
  • Bio age is a dynamic characteristic. Timely treatment and healthy lifestyle can increase biological age indicators, reducing the rate of aging, while inactivity and bad habits can have the inverse effect.

2. Product description

 

 

2.1 Product concept

 

Our product is an application that analyzes locomotive activity data (steps per minute) stored on a mobile phone to accurately estimate a client's biological age, evaluate changes in their BioAge using GeroSense algorithms to calculate their rate of aging, and effectively communicate this information to them to facilitate retirement planning. 

 

2.2 Basic app functionality

 

Data Entry

  • User indicates their birthday and gender, and grant the application access to the HealthKit (Google fit) data package stored on their mobile phone.
  • User can register an account to ensure continuity in their BioAge data in the event of changing their mobile device (without registering an account, data is tied to their device id).
  • User provides information about their current retirement savings, planned future contributions, expected rate of investment returns and an estimate of their current life expenses.
  • User selects their preferred type of benefits (currently annuity or regular payments, in the future options will include online quotes from providers).
     

Processing

  • The application queries HealthKit for step count data (first request - all historical data, later - data generated since the last processed request).
  • Sanitized activity data is sent to a secure server for processing of activity trends, which are used to calculate BioAge.
  • The application receives and saves the calculated bio-age acceleration (BAA) and assessment of the body's response to stress (Resilience)
  • Further analysis of changes in the user’s BioAge allows us to identify trends and generate notifications and recommendations.
     

Personalized Output

  • The application demonstrates current BioAge assessment, as well as past trends of thereof.
  • Based on User’s financial data inputs, the application calculates savings at the time of retirement, and estimated monthly benefits.
  • Monthly benefits are compared with their current life expenses, to allow an accurate evaluation of their retirement goals.
  • The user can modify their planned retirement age to leverage their individual BioAge to instantly evaluate the financial consequences.
  • The application can send regular notifications, alerting the User of changes in their BAA, as well as personalized recommendations regarding their retirement planning.
     

2.3 Commercialization strategies

 

We have a vision to commercialize our idea, targeting middle aged and pre-retirement Individuals, Retirement Advisors and Pension Service Providers. The pension planning tool can exist as a standalone application or be integrated into the pension provider's application. In the case of integration, the provider gets access to consolidated analytics: 

 

  • We will begin commercializing our idea through an initial B2C stage, using a Freemium revenue model. During this period, we will have the opportunity to perfect the UI and fine-tune the notification system. The key value proposition of our product is reliable BioAge measurements and its impact on the client’s retirement perspectives, communicated in an effective and comprehensive way.
     
  • Having secured client interest, and developed an audience we plan to move to the next stage of commercialisation, and work with consultants. We can offer them a platform for regular communication with clients as part of their investment consultations. In this B2B2C channel will implement the PaaS revenue model for Advisors. For this stage, our key value proposition is the opportunity to streamline client-advisor communication.
     
  • In this channel we will offer advisors the opportunity to customize the mobile application to better suit their work, which will expand our client base further. The advisor will gain access to consolidated information about the client’s BioAge as well as its useful trends to further their ability to assist their clients.
     
  • The last stage of our commercialization strategy involves expanding through B2B channels by working with pension providers. Our key value proposition is a “white label” app or SDK that can be integrated with their information systems, allowing them to collect user’s data and generate personalized notification.
     

The pension planning tool can exist as a standalone application or be integrated into the pension provider's application. In the case of integration, the provider gets access to consolidated analytics.

3. Methodology

 

3.1 Confirmation of the BioAge algorithm

 

The current algorithm model is trained on data from the UK Biobank and the US National Health Anxiety Survey (NHANES). As part of the research stage of the development, reliable algorithms were created for the evaluation of Bioage based on DNA, bloodwork, and movement patterns retracted from locomotive activity data. Comparative analysis has then shown a high correlation between the results of Bioage calculations by different methods.

Simple step count data is flawed and insufficient to calculate bio age as it is very “noisy”, requiring further processing to be useful. For this reason, our algorithms do not rely on it, but instead, a more profound analysis takes place where compound patterns within the persons movement are identified from the superficial data, taking into consideration things like intensity, duration and perpetuity within the locomotive data. These patterns are a more trustworthy form of data which offers complex insights into the person’s resilience and capacity for locomotion. This makes it much more effective as training data for machine learning algorithms that calculate BioAge. 

 

3.2 Technical background


The concept of estimating biological age from wearable sensor data has been scientifically validated. Pyrkov et al. (2018, Aging) demonstrated that locomotive activity records can be used to derive a measure of biological age significantly associated with mortality risk and chronic disease incidence. Further validation was provided by Pyrkov, Sokolov, and Fedichev (2021, Aging), who showed that a deep learning model applied to large-scale wearable datasets yields biological age acceleration metrics that correlate with health outcomes and lifestyle factors. In addition, Life & Health Metrics has collected and analyzed over 50,000 individual activity tracks via its research application, which independently confirm the robustness and reproducibility of this approach:

  • The biological age estimates of users form a normal distribution;
  • Non-linear correlation between biological age estimates, motor activity and age;
  • Individual user tracks, on the one hand, are quite stable, but on the other hand, they show fluctuations relative to the average value;
  • Changes in the dynamics of BioAge assessment can be considered as signals for generating feedback to the user
  • It is possible to cluster users by standard deviation of biological age;

              
More detailed scientific and methodological summary is available for download.

Product development may require more precise tuning of the algorithm according to risk groups and underwriting criteria. In the first stage, we envisage conducting pilots with financial institutions - using traditional approaches and the new methodology in parallel until sufficient data is accumulated to confirm the reliability and quality of the BioAge assessment.

 

3.3 Financial modeling

 

For MVP purposes, we use "simple math" for financial modeling, which will be refined as the product undergoes further development. Some of the simplifications used in our product are:

  • estimating the accumulation period based on user data upon reaching retirement age;
  • calculating total retirement savings based on user’s declared current savings and future planned contributions, as well as interest based on user-chosen level of investment return;
  • calculating the monthly payouts based on the payment scheme selected by the user;
  • comparing the estimated pension payout amount with the current level of monthly expenses estimated by the user to help the client in assessing the achievability of pension goals.

As the product undergoes further development and we interact with pension advisors, we will gradually refine the financial calculation model, including:

  • increasing the number of variables, such as inflation, contribution growth rates, expenses, etc.;
  • implementing a more comprehensive approach to determining future investment return, such as using historical data on profitability corresponding to various investment strategies;
  • increasing the options for the payment scheme, including average market parameters of annuities, as well as online quotes from providers;
  • adding family plans that require an expansion of source data and changes in output formats.

4. Product advantages

 

4.1. Encouraging Healthier Client Behaviors

 

Our financial product integrates BioAge feedback to promote healthier lifestyles among clients. By providing real-time insights the tool highlights the impact of lifestyle choices on long-term health and retirement outcomes. As BioAge is a dynamic metric influenced by behavior, clients are motivated to adopt healthier habits, which can lead to improved health outcomes and a potential reduction in BioAge, aligning their retirement planning with enhanced well-being.

 

4.2. Enhanced Marketing and Client Engagement

 

Pension advisers and service providers gain a competitive edge by offering a product that emphasizes client health and longevity. By positioning themselves as partners in their clients’ long-term well-being, providers can strengthen their brand appeal. The message of “helping clients live longer, healthier lives” resonates strongly with consumers, fostering trust and loyalty while differentiating providers in a crowded market.

 

4.3. Improved Risk Management for Aging Populations

 

Aging is a global trend, but the rate of aging varies across client portfolios. Our BioAge-based approach enables providers to compare individual client trajectories with broader demographic trends, offering more precise insights into life expectancy and retirement planning needs. This allows for better management of aging-related risks, optimized reserve calculations, and the potential to unlock new business opportunities through tailored financial products.

 

4.4. Alignment with ESG and Sustainable Development Goals

 

Our solution supports Environmental, Social, and Governance (ESG) objectives by contributing to the United Nations’ Sustainable Development Goal of ensuring healthy lives and promoting well-being for all ages. By empowering clients with tools to monitor and improve their health, pension advisers and providers can align their offerings with sustainable, client-centric goals, enhancing their reputation as socially responsible organizations.

 

For more information about this product, please contact us info@lhmetrics.com