Symptom trajectories have been historically characterized by sporadic and mostly visible data captured during clinical visits. This approach, which dominates our current healthcare models relies on patients subjective reporting of symptoms, and typically waiting until individuals are sick enough to make an appointment to see a doctor or worse, having to go to the hospital. Prevention is currently and has been a buzz word, yet our approaches to medicine are still stuck in an acute care clinical setting.
Further, disease is defined crudely – encompassing arbitrary bins of symptoms that add up to a diagnosis. We know that chronic conditions are not one size fits all, which results in large proportions of patients being missed, or being provided with an ineffective treatment. The recent advancements in wearable technology to detect objective signs of stress and symptoms of disease provides us with an exciting opportunity to use this technology to understand individual trajectories of disease that are true to an individual, not averaged or forced to fit into a predefined box and can be used at the comfort of one’s home and during their daily lives with minimal interference. We aim to detect and forecast individual symptom transitions and shift trajectories of health to those which cannot be confined to the standard office clinical visit. The data we are collecting across studies will essentially allow us and others to build comprehensive metabolic maps for individuals which could provide new insights into disease progression, etiology and perhaps how we conceptualize health and disease.