Physical fitness is it an outcome of a process that is dependent on many variables- some which we could control, and others which we do not. Variables which at first glance seem to be under our direct control include the food and levels of physical activity. However, a quick examination of these two levers reveals the illusion of control. We seldom remember the quantities we eat and often forget the exact calorie count. We make spur of the moment decisions without accounting for the nutritional impact of a cheat day on our overall wellbeing. We have no way of objectively knowing if it\u2019s better to do 20 press ups today and then rest or do 25 continuously.<\/p>\n
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Founded in 2005 by Albert Lee and Mike Lee1<\/sup>, MyFitnessPal is a website and mobile app platform that attempts to solve the fitness challenge by tracking both diet and exercise patterns of users. It synchronizes with wearable technology products and other tracking apps to allow all exercise information to be uploaded from multiple sources.2<\/sup> MyFitnessPal analyzes exercise and dietary patterns and converts the data into meaningful information which enables users to make better decisions about their overall health and fitness. In 2015, MyFitnessPal was acquired by Under Armour3 <\/sup>and added to its Connected Fitness lineup, an umbrella through which Under Armour is able to reach 160 million users3<\/sup>. MyFitnessPal added 80 million4<\/sup> users into the Under Armour fold, along with new technology-based opportunities for the sports apparel manufacturer.<\/p>\n The role of machine learning within MyFitnessPal has evolved as the company has grown from a startup. Initially, MyFitnessPal crowdsourced food information, relying on individuals to input data about their diet into the system5<\/sup>. Machine learning about the individual user allowed the platform to find patterns in the user\u2019s dietary habits- thus, it would start to maintain a record for frequently eaten food and recently eaten food to speed up future data entry. MyFitnessPal made it easy to input data into the platform by pre-loading a lot of information about nutritional value available in food, e.g. a sandwich at Starbucks. 6<\/sup> After lowering some of the barriers to regular input of dietary information, the next machine learning challenge the MyFitnessPal took on was process of creating \u201cverified foods\u201d. Based on having a large number of entries of the nutritional value of a certain type of food, MyFitnessPal could more accurately predict the actual calorific contribution and eliminate the effect of human error in data entry. According to Chul Lee, the unit’s head of data engineering and science, MyFitnessPal also ran a food categorization project as part of training its AI7<\/sup>.<\/p>\n Most recently, under Under Armour, is leveraging data from MyFitnessPal through a partnership with IBM Watson to ensure that users of this app platform can access research-based recommendations on how to improve their sleep, exercise and nutrition2<\/sup>. The machine learning megatrend is significant for MyFitnessPal because it allows the platform to become what it was named for: a true fitness companion that is better at supporting a user\u2019s fitness levels and health.<\/p>\n MyFitnessPal has shown that it can use data science to help users to consistently take the guesswork out of their dietary process. Having overcome some of the challenges associated with the fallibility of human memory, and the amount of analysis need to convert data from food labels into meaningful information, MyFitnessPal platform can go further to support users by integrating with other suppliers in the health journey. The online platform retains your information longer than any personal trainer could and could even serve as a bridge when you move between personal trainers. One could envision a world where MyFitnessPal discovered your dietary patterns and integrated with e-commerce platforms like Amazon to ensure that the foods you ate often were ordered and delivered. Using your body type and composition alongside data from their thousands of other users, MyFitnessPal could predict the likelihood of success of fitness trends e.g. ketogenic diets, HIIT or mindfulness. The exercise arm of MyFitnessPal could integrate with health insurance platforms and allow users who engaged in more health-seeking behaviors to receive reductions in premiums or rebates as a tangible reward to incentive self-care.<\/p>\n <\/p>\n As we think through the possible future applications of machine learning in the digital fitness space, two key questions remain that need to be considered. The world of personal fitness relies on trust. First, how might user information, gathered over extensive periods of time, remain secure as different companies share this information in a bid to improve predictive power? Secondly, how might supervised and unsupervised learning process change as we extend AI-supported fitness products from humans to their pets? (772 words)<\/p>\n Footnotes<\/p>\n https:\/\/www.youtube.com\/watch?v=fu9RKqlmD1Q<\/a>, Accessed 13th<\/sup> November 2018<\/p>\n <\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" We eat and exercise and hope to stay fit. MyFitnessPal takes the guesswork out of the equation so you can get the results you want.<\/p>\n","protected":false},"author":11812,"featured_media":36231,"comment_status":"open","ping_status":"closed","template":"","categories":[209,2120,346],"class_list":["post-36230","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-fitness","category-health-and-fitness","category-machine-learning","hck-taxonomy-organization-myfitnesspal","hck-taxonomy-industry-health","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\n\n
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