Discoveries in biology, genetics, epigenetics, biotypology, and medicine are revealing that the best approach to being healthy and staying that way is to have a diet that is right for your body (1). What works for an “average” person may – or may not - be optimum for you.
So how do you know what’s right for you?
Welcome to the future of healthcare, where mHealth diet applications will come to the rescue. While today’s apps are rudimentary and require a lot of manual input, technology advances are making dietary apps highly advanced, automated and tailored.
The ideal app of the future will reconcile individual human physiology, and its adaptation to changes in environment and lifestyle, to provide more complete, detailed and personalized recommendations for staying well and reaching health goals (2).
Emerging technology will combine algorithms that calculate the risk of disease, monitor current lifestyle habits and health trends, and predict a future trajectory with recommendations of best practices for disease prevention or management. Genetic and phenotypic factors will be used to calculate health risks, and identify trends to provide tailored protocols. Wearable technology will monitor and signal important biological functions, and the continuous data collection will increase computer learning that further refines the technology. New discoveries will automatically update these systems so that users feel more confident and minimize faddism.
Though it seems like all of this is far into the future, it’s actually not. Sophisticated applications that consider a holistic approach to preventative medicine through such technology are already emerging.
Matt Riemann, suffered from a rare genetic condition called Familial Amyloid Polyneuropathy. This causes nerve dysfunction and has a life expectancy of approximately 10 years after onset.
In the course of collaborating with many specialists, scientists, geneticists and others, Matt not only overcame his condition but created ph360. With the premise that each person is unique, the ph360 platform guides a personalized approach to dietary health.
ph360 was launched two years ago, and after accounting for body measurements, genetic data, health history, and lifestyle, aggregates 10,000 data points and more than 500 ratios to recommend personalized food, fitness and lifestyle changes that achieve optimal health.
Shae, is built on the ph360 program.
First, body shape and structure are measured to gain insight on morphology, biotypology, and genetics. Research in epigenetics, for instance, has found that height is associated with cardiovascular conditions (3), digestive health (4) and even cancer (5). Waist circumference is related to cardiovascular risk (6) and diabetes (7). Various body ratios, such as height to weight, have been medically associated with increased risk of osteoporosis (8), certain metabolic conditions (9) and important hormone levels (10).
Health surveys are also used to get a better gauge of health risks. For example, skin and hair color is associated with the risk of sun damage (11), nail structure can indicate mineral deficiencies (12), and lifestyle choices can increase or decrease the likelihood of disease onset or progression (13, 14). Chronobiology (15) and the natural human aging process are considered (16) to provide insights on how sleep and stress affect health and well being (17) or how health risks may increase or change with age (18).
Shae takes ph360’s insights one step further by providing 24-7 support for ph360 users as a “Virtual Health Assistant.” It’s being engineered to use interactive voice and text conversations to communicate a personalized health plan with users in real time via their phone, tablet, laptop or smartwatch. Shae will connect with wearables and analyze a user’s data to make practical recommendations regarding diet, exercise, and lifestyle activities that directly influence their health.
Following users through their day and responding as circumstances – such as environment, activity, diet and stress levels change, these are some of the things that Shae will communicate:
• Recommended specific foods ideal for the person, indicate why and provide nutrient information, recipes and shopping lists for the recommended foods that the user selects.
• Recommended the very best exercises for the individual’s fitness goals and specific body type, the ideal time of day to exercise and best sports to play.
• How to integrate Geomedicine through GPS, making recommendations for foods, activities, transportation and more based on where the person is in the world.
• How to optimize your schedule based on body rhythm to help minimize stress and increase productivity.
Shae has been funded on Kickstarter and is currently being funded on Indiegogo. Version 1.0 will be available in October 2016. Upgrade versions will be released every few months with version 1.5 arriving in July 2017. The upgrades are all covered in the original purchase price.
1. Ferguson, L. R., et al. "Guide and Position of the International Society of Nutrigenetics/Nutrigenomics on Personalised Nutrition." Journal of Nutrigenetics and Nutrigenomics 9.1 (2016): 12-27.
2. Ferguson, Lynnette R., ed. Nutrigenomics and nutrigenetics in functional foods and personalized nutrition. CRC Press, 2013.
3. Lee, Crystal Man Ying, et al. "Adult height and the risks of cardiovascular disease and major causes of death in the Asia-Pacific region: 21 000 deaths in 510 000 men and women." International Journal of Epidemiology (2009): dyp150.
4. Asao K, Kao WH, Baptiste-Roberts K, et al. Short stature and the risk of adiposity, insulin resistance, and type 2 diabetes in middle age: the Third National Health and Nutrition Examination Survey (NHANES III), 1988–1994. Diabetes Care 2006;29:1632–7.
5. Kabat, Geoffrey C., H. Dean Hosgood III, and Thomas E. Rohan. "Adult Height in Relation to the Incidence of Cancer at Different Anatomic Sites: the Epidemiology of a Challenging Association." Current Nutrition Reports 5.1 (2016): 18-28.
6. Nazare, Julie-Anne, et al. "Usefulness of measuring both body mass index and waist circumference for the estimation of visceral adiposity and related cardiometabolic risk profile (from the INSPIRE ME IAA study)." The American Journal of Cardiology 115.3 (2015): 307-315.
7. Chamnan, Parinya, Hansa Choenchoopon, and Suvit Rojanasaksothorn. "Abstract MP93: Waist Circumference Has a Stronger Association With Diabetes Than Body Mass Index: Results From a Large Health Examination of 355,310 Thai Men and Women." Circulation 131.Suppl 1 (2015): AMP93-AMP93.
8. Asomaning, Kofi, et al. "The association between body mass index and osteoporosis in patients referred for a bone mineral density examination." Journal of Women's Health 15.9 (2006): 1028-1034.
9. Jacobsson, J. A., et al. "Genetic variants near the MGAT1 gene are associated with body weight, BMI and fatty acid metabolism among adults and children." International Journal of Obesity 36.1 (2012): 119-129.
10. Osuna C, J. A., et al. "Relationship between BMI, total testosterone, sex hormone-binding-globulin, leptin, insulin and insulin resistance in obese men." Archives of Andrology 52.5 (2006): 355-361.
11. Veierød, Marit Bragelien, et al. "Sun and solarium exposure and melanoma risk: effects of age, pigmentary characteristics, and nevi." Cancer Epidemiology Biomarkers & Prevention 19.1 (2010): 111-120.
12. Cashman, Michael W., and Steven Brett Sloan. "Nutrition and nail disease." Clinics in Dermatology 28.4 (2010): 420-425.
13. Roberts, Christian K., and R. James Barnard. "Effects of exercise and diet on chronic disease." Journal of Applied Physiology 98.1 (2005): 3-30.
14. Moritani, Toshio. "The Role of Exercise and Nutrition in Lifestyle-Related Disease." Physical Activity, Exercise, Sedentary Behavior and Health. Springer Japan, 2015. 237-249.
15. Lloyd, David, and Ernest L. Rossi, eds. Ultradian rhythms in life processes: An inquiry into fundamental principles of chronobiology and psychobiology. Springer Science & Business Media, 2012.
16. Lin, Jue, Elissa Epel, and Elizabeth Blackburn. "Telomeres and lifestyle factors: roles in cellular aging." Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 730.1 (2012): 85-89.
17. Mullan, Barbara A. "Sleep, stress and health: A commentary." Stress and Health 30.5 (2014): 433-435.
18. Singh, Gitanjali M., et al. "The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis."PloS One 8.7 (2013): e65174.