Mobile devices present the opportunity to continuously collect health data including movement and walking speed. Fitness trackers have become popular which record steps taken, distance walked and caloric expenditure. While useful for fitness purposes, medical monitoring requires precise accuracy and testing on real patients with a medically valid measure. Walking speed is closely linked to morbidity in patients and is also useful to determine distance walked during sixminute walk tests, a standard assessment for both chronic obstructive pulmonary disease and congestive heart failure. Current generation smartphone hardware contains similar sensor chips as medical devices and popular fitness devices. We develop a middleware, Move Sense, to provide comparable readings to medical accelerometers using only smartphones. We evaluate six methods developed for constrained treadmill walking to obtain gait speed during natural walking with older chronic pulmonary patients and train new models to predict speed and distance. Natural walking is walking without artificial speed constraints introduced during treadmill and nurse assisted walking. We also compare our model's accuracy to popular fitness devices. Our models produce accurate 6MWT distance and higher accuracy distance estimation than dedicated fitness devices during unconstrained walking on patients using a universally trained support vector machine model.