Health IndicesOverview

Health Indices

Shen.AI provides functionalities to compute key indices based on your user’s data to assess their health, body composition, and cardiovascular risk, offering valuable insights to help them better understand their body and make informed health decisions.

1. Wellness Score

2. Health Risk Indices:

  • Vascular Age
  • Cardiovascular Disease Risk
  • Cardiovascular Event Risk
  • Cardiovascular Risk Score

3. Body Composition and Metabolism Indices:

  • Waist-to-Height Ratio (WHtR)
  • Body Fat Percentage (BFP)
  • Body Roundness Index (BRI)
  • A Body Shape Index (ABSI)
  • Conicity Index (CI)
  • Basal Metabolic Rate (BMR)
  • Total Daily Energy Expenditure (TDEE)

How can I use Health Indices?

Shen.AI SDK offers two independent ways to leverage Health Indices:

  1. Embedded UI Flow: As part of the video measurement experience, the end-user can enter the health risk factors in a guided UI flow after the measurement. The SDK then displays the computed indices (e.g., vascular age, body fat %, etc.) directly in your app.
  2. Direct API Calls:
    You can call the Health Indices API (e.g., computeHealthRisks()) standalone if you only need indices based on user-provided data (e.g. age, systolic BP, cholesterol, etc.). However, the Wellness Score relies on certain metrics (e.g., HR, HRV, BP) derived from a successful video measurement scan. If you do not have measured data available (for example, from a completed scan), the Wellness Score portion will not be computed. In the standalone scenario, simply pass in the user’s risk factors, and the SDK returns those risk-based indices programmatically (like vascular age, CVD risk, etc.). You can then display or process them however you wish in your app.

Important: These two approaches are independent. Using the embedded UI flow does not require you to manually call the API, and calling the API methods does not require you to show the embedded UI or run a video measurement first.

Wellness Score

The Wellness Score is a comprehensive and holistic measure designed to evaluate an individual’s overall well-being, encompassing physical, emotional, social, spiritual, and lifestyle dimensions. Unlike “health,” which focuses on the absence of disease, or “well-being,” which is about the balance of resources and challenges, the Wellness Score seeks to reflect an individual’s full potential for optimal living.

Data needed to compute Wellness Score:

The calculation of the Wellness Score involves a wide range of physiological and lifestyle factors, including:

  • Physiological Metrics:
    • Heart Rate (HR)
    • Heart Rate Variability (HRV)
    • Breathing Rate (BR)
    • Blood Pressure (BP)
    • Stress Index
    • Cardiac Workload
    • Body Mass Index (BMI)
  • User-Provided Data:
    • Age
    • Gender
    • Cholesterol Levels (Total and HDL)
    • Diabetes Status (Yes/No)
    • Smoking Status (Yes/No)
    • Hypertension Treatment (Yes/No)

Important: Without a completed video scan providing the needed vital-sign data, the Wellness Score cannot be calculated.

Categorization for the Wellness Score (WS) along with descriptions:

  • 0–20: Critical (Very Low Wellness)
    This range indicates a critical state of wellness, often characterized by multiple significant health risks or conditions. Immediate medical attention and lifestyle changes are likely required to improve overall health.

  • 21–40: Poor (Low Wellness)
    Scores in this range reflect below-average wellness, with several health parameters in the suboptimal range. Meaningful interventions (e.g. better diet, exercise, or medical management) are necessary.

  • 41–60: Moderate (Average Wellness)
    This range represents moderate wellness. While there may not be immediate critical risks, there is considerable room for improvement.

  • 61–80: Good (High Wellness)
    Scores in this range indicate above-average health and wellness. Most key health metrics are near-optimal. Minor adjustments can further enhance overall wellness.

  • 81–100: Excellent (Very High Wellness)
    This range reflects excellent wellness. Key health parameters are in ideal ranges, and the individual likely has strong protective factors against chronic disease and aging-related risks.

Health Risk Indices:

Vascular Age

Vascular age is calculated as the age of a person with the same predicted overall risk for developing atherosclerotic cardiovascular disease (CVD), but with all risk factors at normal levels (total cholesterol 180 mg/dL, HDL 45 mg/dL, untreated systolic blood pressure 125 mm Hg, no diabetes, no smoking).

Note: The vascular age model is based on studies with subjects aged 30 to 74. Therefore, for individuals younger or older than that range, the estimated vascular age might be artificially higher or lower than their true value.

Data needed to compute vascular age:

  • age
  • gender
  • current smoking status
  • diabetes (fasting glucose > 125 mg/dL or use of insulin or hypoglycemic medications)
  • treated hypertension
  • systolic blood pressure
  • total cholesterol level and HDL level or body height and weight

Depending on which data is provided, the calculations are done either based on cholesterol levels (more accurate) or on body mass index (less accurate).

Resource:

  1. Framingham Heart Study, D’Agostino RB, et al., Circulation. 2008;117(6):743–753.

Cardiovascular Disease Risk

The estimated 10-year risk of atherosclerotic cardiovascular disease is based on multivariable, gender-specific risk prediction algorithms derived from the well-known Framingham Heart Study (Framingham risk functions). These functions consider age, high systolic blood pressure (treated or untreated), dyslipidemia (total and high-density lipoprotein cholesterol) or high body mass index (BMI), smoking, and diabetes (D’Agostino et al., Circulation, 2008;117:743-753). The Framingham risk functions are primarily based on data from Caucasian/white populations; therefore, for other races, the actual risk may differ. In particular, compared to Caucasian/white populations, cardiovascular risk is generally higher for African American and American Indian populations, and lower for Hispanic/Latino and Asian populations.

Note: The CVD risk model is based on studies with subjects aged 30 to 74. Therefore, for individuals younger or older than that range, the estimated risk might be artificially higher or lower than their true risk.

The following risks are computed:

  • Atherosclerotic cardiovascular disease (CVD) — overall risk
  • Specific CVD risks:
    • Coronary heart disease (myocardial infarction, coronary death, coronary insufficiency, angina)
    • Stroke (ischemic stroke, hemorrhagic stroke, transient ischemic attack)
    • Heart failure
    • Peripheral vascular disease (intermittent claudication)

Data needed to compute CVD risk:

  • age
  • gender
  • current smoking status
  • diabetes (fasting glucose > 125 mg/dL or use of insulin or hypoglycemic medications)
  • treated hypertension
  • systolic blood pressure
  • total cholesterol level and HDL level or body height and weight

Depending on which data is provided from the last point, risks are computed either based on cholesterol level (more accurate) or on body mass index (less accurate).

Resource:

  1. Framingham Heart Study, D’Agostino RB, et al., Circulation. 2008;117(6):743–753.

Cardiovascular Event Risk

The estimated risk of a first hard atherosclerotic cardiovascular event in the next 10 years (coronary death, myocardial infarction, or stroke) for someone without CVD. The estimation is based on the race- and gender-specific Pooled Cohort Equations developed in 2013 by the Risk Assessment Work Group of the American College of Cardiology/American Heart Association, using a combination of risk factors (age, high systolic blood pressure (treated or untreated), dyslipidemia (total and high-density lipoprotein cholesterol), smoking, and diabetes) examined in several cohorts of patients in the USA (Goff Jr et al. Circulation, 2014;129(25 Suppl 2):S49-73). For races other than Caucasian/white or African American, the risk estimation is based on the model validated on Caucasian/white populations; hence the actual risk may differ. In particular, compared to Caucasian/white populations, the cardiovascular risk is generally higher for American Indian populations and lower for Hispanic/Latino and Asian populations.

The following risks are computed:

  • Hard CV events (coronary death, myocardial infarction, or stroke) — based on Pooled Cohort Equations (USA)
  • Fatal CV events (European SCORE):
    • Coronary death
    • Fatal stroke
    • Total cardiovascular mortality (coronary + stroke)

Note: The hard CV events risk model is based on studies with subjects aged 40 to 79, and the fatal CV events risk model on subjects aged 40 to 65. Therefore, for individuals outside those age ranges, the estimated risk might be artificially higher or lower than their true risk.

Data needed to compute CV event risks:

  • age
  • gender
  • current smoking status
  • diabetes status (fasting glucose > 125 mg/dL or use of insulin or hypoglycemic medications) — only for hard CV events
  • treated hypertension (only for hard CV events)
  • systolic blood pressure
  • total cholesterol level
  • HDL level (only for hard CV events)
  • race (only for hard CV events)
  • country (only for fatal CV events)

Resource:

  1. Goff Jr DC et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation, 2014;129(25 Suppl 2):S49-73.

Cardiovascular Risk Score

In addition to computed risks, Shen.AI can provide information on how much each factor contributes to the overall risk of developing CVD, presented in a scoring format.

Scores computed for factors:

  • age
  • current smoking status
  • diabetes
  • systolic blood pressure
  • total cholesterol level and HDL level (or BMI)
  • total risk score

Resource:

  1. Framingham Heart Study, D’Agostino RB, et al., Circulation. 2008;117(6):743–753.

Body Composition and Metabolism Indices:

Waist-to-Height Ratio (WHtR)

Waist-to-Height Ratio (WHtR) is a metric that estimates the risk of obesity-related conditions, such as cardiovascular diseases, diabetes, and metabolic syndrome, by assessing abdominal fat distribution through the ratio of waist circumference to height. Higher WHtR values indicate greater abdominal fat, which is associated with increased health risks.

Note: Depending on the available data, WHtR may either be directly calculated from waist circumference and height or estimated using regression models based on demographic data

Data needed to compute WHtR:

  • age
  • gender
  • height
  • weight
  • ethnicity

or

  • height
  • waist circumference

Ranges for WHtR

  • Normal Range:
    • For both men and women, a WHtR of less than 0.5 is generally considered healthy.
  • Increased Risk:
    • A WHtR of 0.5 or higher suggests an increased risk of health issues related to obesity and should be taken seriously.

Resources:

  1. Gibson, Sigrid & Ashwell, Margaret. (2019). A simple cut-off for waist-to-height ratio (0·5) can act as an indicator for cardiometabolic risk: Recent data from adults in the Health Survey for England. British Journal of Nutrition. 123. 1-26. 10.1017/S0007114519003301.
  2. Eslami M, Pourghazi F, Khazdouz M, Tian J, Pourrostami K, Esmaeili-Abdar Z, Ejtahed HS, Qorbani M. Optimal cut-off value of waist circumference-to-height ratio to predict central obesity in children and adolescents: A systematic review and meta-analysis of diagnostic studies. Front Nutr. 2023 Jan 4;9:985319. doi: 10.3389/fnut.2022.985319. PMID: 36687719; PMCID: PMC9846615.

Body Fat Percentage (BFP)

Body Fat Percentage (BFP) estimates the proportion of fat in the body relative to total body weight. It is an important indicator of overall health and fitness, helping to assess the risk of obesity-related conditions such as heart disease, diabetes, and metabolic syndrome. A higher body fat percentage is associated with an increased risk of cardiovascular disease. Understanding BFP helps users monitor their health status and encourages lifestyle changes to reduce fat levels.

Data needed to compute Body Fat Percentage (BFP):

  • age
  • gender
  • height
  • weight

Ranges for BFP:

  • Essential Fat:
    • Men: 2-5%
    • Women: 10-13%
  • Athletes:
    • Men: 6-13%
    • Women: 14-20%
  • Fitness:
    • Men: 14-17%
    • Women: 21-24%
  • Acceptable:
    • Men: 18-24%
    • Women: 25-31%
  • Obese:
    • Men: 25% and higher
    • Women: 32% and higher

Resource:

  1. Ideal Body Fat Percentage chart (American Council on Exercise)

Body Roundness Index (BRI)

The Body Roundness Index (BRI) estimates the distribution of body fat—particularly abdominal fat—by considering waist circumference and height. Unlike BMI, BRI provides a more detailed understanding of fat distribution and its implications for cardiovascular risk and metabolic health. BRI values typically range from 1 to 16, with higher values indicating more abdominal fat.

Data needed to compute BRI:

  • age
  • gender
  • height
  • weight
  • ethnicity

or

  • height
  • waist circumference

Ranges for BRI: BRI values range from 1 to 16, with higher values indicating rounder body shapes (higher abdominal fat) and lower values representing leaner individuals.

Research supports BRI as a reliable predictor of health risks:

  • Optimal cutoff points:
    • Men: BRI = 3.85 (Sensitivity: 76.5%, Specificity: 82.1%)
    • Women: BRI = 4.05 (Sensitivity: 76.4%, Specificity: 70.3%)

Resources:

  1. Endukuru CK, Gaur GS, Dhanalakshmi Y, Sahoo J, Vairappan B. Cut-off values and clinical efficacy of body roundness index and other novel anthropometric indices in identifying metabolic syndrome and its components among Southern-Indian adults. Diabetol Int. 2021 Jul 18;13(1):188-200. doi: 10.1007/s13340-021-00522-5. PMID: 35059255; PMCID: PMC8733072.
  2. Liu B, Liu B, Wu G, Yin F. Relationship between body-roundness index and metabolic syndrome in type 2 diabetes. Diabetes Metab Syndr Obes. 2019 Jun 19;12:931-935. doi: 10.2147/DMSO.S209964. PMID: 31354325; PMCID: PMC6590402.

A Body Shape Index (ABSI)

The A Body Shape Index (ABSI) gauges obesity-related health risks by emphasizing waist circumference and distribution of body fat. It differs from BMI by focusing specifically on the relationship between waist circumference, height, and weight—helpful in identifying risks associated with abdominal obesity.

Data needed to compute ABSI:

  • age
  • gender
  • height
  • weight
  • waist circumference

Ranges for ABSI

  • If the ABSI is above 0.083, an increased risk is assumed;
  • A value of 0.091 is said to represent a doubling of the relative risk, so:
    • Normal Range: ABSI < 0.083
    • Increased Risk: ABSI > 0.083 but < 0.091
    • High Risk: ABSI > 0.091 (doubling of relative risk)

Resource:

  1. Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS One. 2012;7(7):e39504. doi: 10.1371/journal.pone.0039504. Epub 2012 Jul 18. PMID: 22815707; PMCID: PMC3399847.

Conicity Index (CI)

The Conicity Index (CI) estimates abdominal obesity based on waist circumference, weight, and height. Higher CI values signify greater visceral fat accumulation, correlating with elevated risks of metabolic conditions.

Data needed:

  • age
  • gender
  • height
  • weight
  • waist circumference

Ranges for CI: CI values range between 1.0 (a perfect cylinder) and 1.73 (a perfect double cone), with higher values reflecting greater abdominal fat accumulation:

  • Normal Range: CI < 1.275 for men, CI < 1.285 for women
  • High Risk: CI ≥ 1.275 for men, CI ≥ 1.285 for women

The closer the CI value is to 1.73, the greater the accumulation of abdominal fat, indicating a higher risk of metabolic and cardiovascular complications.

Resource:

  1. Martins CA, do Prado CB, Santos Ferreira JR, Cattafesta M, Dos Santos Neto ET, Haraguchi FK, Marques-Rocha JL, Salaroli LB. Conicity index as an indicator of abdominal obesity in individuals with chronic kidney disease on hemodialysis. PLoS One. 2023 Apr 19;18(4):e0284059. doi: 10.1371/journal.pone.0284059. PMID: 37075008; PMCID: PMC10115262.

Basal Metabolic Rate (BMR)

Basal Metabolic Rate (BMR) is the number of calories your body requires at rest to maintain basic physiological functions, such as breathing, circulation, and cell production. BMR accounts for a significant portion of total daily energy expenditure (TDEE) and is influenced by factors such as age, gender, weight, height, and body composition.

BMR doesn’t have strict “normal ranges” like some other health metrics (e.g., blood pressure or cholesterol levels) because it is highly individual. However, understanding your BMR helps assess whether your energy needs are typical for your age, gender, and body composition.

Data needed to compute Basal Metabolic Rate (BMR):

  • age
  • gender
  • weight
  • height

Total Daily Energy Expenditure (TDEE)

The Total Daily Energy Expenditure (TDEE) represents the total number of calories your body uses in a day to perform all activities, including basic physiological functions (BMR), physical activity, digestion, etc. TDEE is crucial for understanding calorie requirements and planning dietary/fitness goals (weight loss, maintenance, or muscle gain).

Data needed to compute TDEE:

  • age
  • gender
  • weight
  • height
  • physical activity level
CalibrationIntegrating indices functionality
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