Risk Review
The outcome of the Risk Review is to make sure that the risk shown matches the user's ethnicity, age, and gender (or other identifying markers) of the study used.
Partners are responsible to ensure the Risk Study is suitable for it's users.
Partners must be aware of their demographic when calculating risk to make sure the study matches the user's ethnicity, age, and gender (or other identifying markers). Health risks are not a "One size fits all" approach. Ensure the study matches the people who will use the app. Doing so will ensure an accurate and trusted product.
By the end of the risk review, the following points should be completed or covered:
Understanding Risks: Not all Risks are created equal.
Risks & indicators: Confirm each risk or indicator study matches your user audience.
Disclaimer: Addition of disclaimer to app screens where risk is shown.
Tracking Risks: Understanding Trends (coming soon).
Integrating Risks: Information for Users (coming soon).
Health Risks are the result of applying a study to a set of scan result(s).
Almost all of the studies used to predict risk are population-based. Researchers gather data related to a specific condition, such as ethnicity, age, height, weight, diet, sleep, family history, blood tests, and other relevant data.
The results are published showing a strong or weak correlation of this data used for prediction.
If the study were conducted in North Africa, the data would be very accurate and applicable to North Africans. If applied to Europeans/Caucasians, it might not be as accurate. A study that uses European/Caucasians as a cohort would better suit that demographic.
Similarly, if the study involved both Men and Women over 45 years of age, it is likely to be less effective to those under 45.
This step involves making sure the study matches your audience. The predictors are the main lead on how this is accomplished. Where ethnicity is not a predictor, it is likely combined where the difference is small.
- Default Risks: By default, all risks are tailored to a global study (WHO, IDF) or the North American population.
- Geographic Locations: If your app is being released in different geographic locations, it is recommended to use different studies for that unique population to categorize risk. e.g. One for Australia - Aboriginal & Torres Strait Islanders, Canada & Alaska – Aboriginal Inuits, etc.
- Likewise, where a study might include data for both sexes, there might be a specific study for females. It is advised to be specific and use the best study for that sex if it is available.
- Conduct Frequent Risk Reviews: Studies improve over time. It is advised to do frequent internal Risk Reviews so that the predictions are current, and reflect the latest data available.
Obesity is a direct relationship to Total Body Fat. It is then categorized by age and gender.
Data Sheet | |
About | Overweight and obesity are defined as abnormal or excessive fat accumulation that presents a risk to health. – https://www.who.int/health-topics/obesity#tab=tab_1 |
Predictors | by AGE by SEX by TOTAL BODY FAT |
Study | Heo, M., Faith, M. S., Pietrobelli, A., & Heymsfield, S. B. (2012). Percentage of body fat cutoffs by sex, age, and race-ethnicity in the US adult population from NHANES 1999–2004. The American journal of clinical nutrition, 95(3), 594-602. |
Information | The study does split by ethnicity, accounting for only a small difference between the races. By using the average of all cutoffs and not asking for ethnicity it can reduce identifiable and race-related issues. However, partners who have that demographic can use the exact dataset to be closer to the published paper. |
Central Obesity is a direct relationship to Waist Circumference. It is then categorized by Ethnicity and Gender.
Data Sheet | |
About | Abdominal obesity, also known as central obesity and truncal obesity, is a condition when excessive abdominal fat around the stomach and abdomen ... https://www.wikiwand.com/en/Abdominal_obesity |
Predictors | by SEX by WAIST CIRCUMFERENCE by ETHNICITY Europids, South Asians, Chinese, Japanese, Ethnic South and Central Americans, Sub-Saharan Africans, Eastern Mediterranean and Middle East (Arab) populations. |
Study | World Health Organization. (2011). Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8-11 December 2008. International Diabetes Federation. Alberti, G., Zimmet, P. Z., Shaw, J., & Grundy, S. M. (2006). The IDF consensus worldwide definition of metabolic syndrome. |
Information | Defining central obesity with a simple sex-specific waist circumference threshold provides a simple diagnostic and clinical tool to define those who are potentially at greater risk of medical comorbidities, detect them early and facilitate intervention. Universal cutoffs, covering all ethnicities, are not currently available due to limited research. There are inherent challenges related to the determination of health outcomes, including sex differences; age‐related changes in body composition and conformation; and group, population, and geographical differences. These confounders need to be evaluated more carefully before consensus cutoffs can be reported. |
Type-2 Diabetes requires self-reported information such as diet, family history, physical activity, and information from the BodyScan (waist circumference). This depends on the risk assessment being used, and what can be filled in by a Face or Body Scan.
It is usually population-based, such that the weighting system involved will account for the racial demographic of its occupants.
Data Sheet | |
Risk Calculators | |
Predictors (vary based on Calculator) | by AGE by WEIGHT by SEX by FAMILY HISTORY by DIET by ACTIVITY by ETHNICITY by SMOKER |
Study | n/a |
Information | IDF preferred. |
Waist Circumference is a key indicator in multiple risk predictions, in multiple studies. Some key areas where Waist Circumference contributes to risk prediction:
- Indicators of abdominal adiposity, especially WHtR, are more strongly associated with stroke risk than BMI. – https://www.ahajournals.org/doi/full/10.1161/STROKEAHA.111.614099
- A waist circumference ≥94 cm in middle-aged men, identified those with increased risk for type 2 diabetes and/or cardiovascular disease. – https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-12-631
- Predictor for Metabolic Syndrome: To have the metabolic syndrome, a person must have central adiposity defined on the basis of waist circumference and... – https://care.diabetesjournals.org/content/28/11/2745
It is advised to use Waist Circumference:
- Where cardiovascular health or metabolic syndrome is included, highlight its importance along with waist-hip and waist-height indicators.
The Waist-Hip Ratio is a direct relationship to waist and hip circumference. It is categorized by ethnicity and sex.
Data Sheet | |
About | The waist-Hip ratio is used to measure risks of chronic disease and mortality. A larger waist to hip ratio indicates preferential fat storage around the waist in the form of visceral adipose tissue, which can be associated with increased disease and mortality risk. |
Predictors | by WAIST CIRCUMFERENCE by HIP CIRCUMFERENCE by SEX by ETHNICITY United States, Europids, Middle East, African, South and Central Americans, South Asian, Chinese, Japanese |
Study | Waist circumference and waist-hip ratio: report of a WHO expert consultation – World Health Organization |
Information | |
The Waist-Hip Ratio is a direct relationship to waist circumference and height (self-reported). It is categorized by sex.
Data Sheet | |
About | A larger waist-to-height ratio can be associated with higher levels of abdominal fat in the form of visceral adipose tissue which is linked to increased risks of chronic disease and mortality. It is recommended to keep your waist circumference to less than half your height for improved health. |
Predictors | by WAIST CIRCUMFERENCE by HEIGHT by SEX |
Study | Ashwell, M., Gunn, P., & Gibson, S. (2012). Waist‐to‐height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta‐analysis. Obesity Reviews, 13(3), 275-286. |
Information | As height is self-reported, it might suffer from human error. Due to the nature of the cutoffs, it might have negligible effects. |
Smartphone-based imaging systems for fitness, health, and medical applications are relatively new, despite the smartphone being widely used for a range of medical purposes.
CompleteScan and its prediction of risks is not been approved as a medical device. A disclaimer must be added to be transparent with end-users.
Smartphone-based imaging systems for medical applications: a critical review Brady Hunt, Alberto J. Ruiz, Brian W. Pogue
